US20150180886A1 - Systems and Methods for Scheduling Analysis of Network Content for Malware - Google Patents

Systems and Methods for Scheduling Analysis of Network Content for Malware Download PDF

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US20150180886A1
US20150180886A1 US14/620,101 US201514620101A US2015180886A1 US 20150180886 A1 US20150180886 A1 US 20150180886A1 US 201514620101 A US201514620101 A US 201514620101A US 2015180886 A1 US2015180886 A1 US 2015180886A1
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network content
data
malicious
network
virtual machine
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US14/620,101
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Stuart Gresley Staniford
Ashar Aziz
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FireEye Inc
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FireEye Inc
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Priority to US12/263,971 priority Critical patent/US8850571B2/en
Priority to US13/925,733 priority patent/US8990939B2/en
Application filed by FireEye Inc filed Critical FireEye Inc
Priority to US14/620,101 priority patent/US20150180886A1/en
Publication of US20150180886A1 publication Critical patent/US20150180886A1/en
Application status is Pending legal-status Critical

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    • HELECTRICITY
    • H04ELECTRIC COMMUNICATION TECHNIQUE
    • H04LTRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L63/00Network architectures or network communication protocols for network security
    • H04L63/14Network architectures or network communication protocols for network security for detecting or protecting against malicious traffic
    • H04L63/1441Countermeasures against malicious traffic
    • H04L63/145Countermeasures against malicious traffic the attack involving the propagation of malware through the network, e.g. viruses, trojans or worms
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRIC DIGITAL DATA PROCESSING
    • G06F21/00Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
    • G06F21/50Monitoring users, programs or devices to maintain the integrity of platforms, e.g. of processors, firmware or operating systems
    • G06F21/55Detecting local intrusion or implementing counter-measures
    • G06F21/56Computer malware detection or handling, e.g. anti-virus arrangements
    • G06F21/561Virus type analysis
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRIC DIGITAL DATA PROCESSING
    • G06F21/00Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
    • G06F21/50Monitoring users, programs or devices to maintain the integrity of platforms, e.g. of processors, firmware or operating systems
    • G06F21/55Detecting local intrusion or implementing counter-measures
    • G06F21/56Computer malware detection or handling, e.g. anti-virus arrangements
    • G06F21/566Dynamic detection, i.e. detection performed at run-time, e.g. emulation, suspicious activities
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRIC DIGITAL DATA PROCESSING
    • G06F21/00Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
    • G06F21/50Monitoring users, programs or devices to maintain the integrity of platforms, e.g. of processors, firmware or operating systems
    • G06F21/55Detecting local intrusion or implementing counter-measures
    • G06F21/56Computer malware detection or handling, e.g. anti-virus arrangements
    • G06F21/567Computer malware detection or handling, e.g. anti-virus arrangements using dedicated hardware
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRIC DIGITAL DATA PROCESSING
    • G06F9/00Arrangements for program control, e.g. control units
    • G06F9/06Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
    • G06F9/44Arrangements for executing specific programs
    • G06F9/455Emulation; Interpretation; Software simulation, e.g. virtualisation or emulation of application or operating system execution engines
    • G06F9/45533Hypervisors; Virtual machine monitors
    • HELECTRICITY
    • H04ELECTRIC COMMUNICATION TECHNIQUE
    • H04LTRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L63/00Network architectures or network communication protocols for network security
    • H04L63/14Network architectures or network communication protocols for network security for detecting or protecting against malicious traffic
    • H04L63/1408Network architectures or network communication protocols for network security for detecting or protecting against malicious traffic by monitoring network traffic
    • HELECTRICITY
    • H04ELECTRIC COMMUNICATION TECHNIQUE
    • H04LTRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L63/00Network architectures or network communication protocols for network security
    • H04L63/14Network architectures or network communication protocols for network security for detecting or protecting against malicious traffic
    • H04L63/1408Network architectures or network communication protocols for network security for detecting or protecting against malicious traffic by monitoring network traffic
    • H04L63/1416Event detection, e.g. attack signature detection
    • HELECTRICITY
    • H04ELECTRIC COMMUNICATION TECHNIQUE
    • H04LTRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L2463/00Additional details relating to network architectures or network communication protocols for network security covered by H04L63/00
    • H04L2463/144Detection or countermeasures against botnets

Abstract

A method for detecting malicious network content comprises inspecting one or more packets of network content, identifying a suspicious characteristic of the network content, determining a score related to a probability that the network content includes malicious network content based on at least the suspicious characteristic, identifying the network content as suspicious if the score satisfies a threshold value, executing a virtual machine to process the suspicious network content, and analyzing a response of the virtual machine to detect malicious network content.

Description

    CROSS-REFERENCE TO RELATED APPLICATIONS
  • This application is a continuation of U.S. patent application Ser. No. 13/925,733 filed on Jun. 24, 2013, which is a continuation of U.S. patent application Ser. No. 12/263,971 filed on Nov. 3, 2008, the entire contents of both of which are incorporated by reference.
  • This application is related to U.S. patent application Ser. No. 11/409,355 entitled “Heuristic Based Capture with Replay to Virtual Machine” and filed on Apr. 20, 2006, which is a continuation-in-part of U.S. patent application Ser. No. 11/152,286 entitled “Computer Worm Defense System and Method” and filed on Jun. 13, 2005, which claims the priority benefit of U.S. Provisional Patent Application Ser. No. 60/579,910 entitled “Computer Worm Defense System and Method” and filed on Jun. 14, 2004. U.S. patent application Ser. No. 11/409,355 is also a continuation-in-part of U.S. patent application Ser. No. 11/096,287 entitled “System and Method of Detecting Computer Worms” and filed on Mar. 31, 2005, which claims the priority benefit of U.S. Provisional Patent Application Ser. No. 60/559,198 entitled “System and Method of Detecting Computer Worms” and filed on Apr. 1, 2004. U.S. patent application Ser. No. 11/409,355 is also a continuation-in-part of U.S. patent application Ser. No. 11/151,812 entitled “System and Method of Containing Computer Worms” and filed on Jun. 13, 2005, which claims the priority benefit of U.S. Provisional Patent Application No. 60/579,953 entitled “System and Method of Containing Computer Worms” and filed on Jun. 14, 2004. Each of the aforementioned patent applications are incorporated by reference herein.
  • BACKGROUND
  • 1. Field of the Invention
  • The present invention relates generally to network security and more particularly to detecting malicious network content.
  • 2. Related Art
  • Presently, malicious network content (e.g., malicious software or malware) can attack various devices via a communication network. For example, malware may include any program or file that is harmful to a computer user, such as bots, computer viruses, worms, Trojan horses, adware, spyware, or any programming that gathers information about a computer user or otherwise operates without permission.
  • Adware is a program configured to direct advertisements to a computer or a particular user. In one example, adware identifies the computer and/or the user to various websites visited by a browser on the computer. The website may then use the adware to either generate pop-up advertisements or otherwise direct specific advertisements to the user's browser. Spyware is a program configured to collect information regarding the user, the computer, and/or a user's network habits. In an example, spyware may collect information regarding the names and types of websites that the user browses and then transmit the information to another computer. Adware and spyware are often added to the user's computer after the user browses to a website that hosts the adware and/or spyware. The user is often unaware that these programs have been added and are similarly unaware of the adware and/or spyware's function.
  • Various processes and devices have been employed to prevent the problems that malicious network content can cause. For example, computers often include antivirus scanning software that scans a particular client device for viruses. Computers may also include spyware and/or adware scanning software. The scanning may be performed manually or based on a schedule specified by a user associated with the particular computer, a system administrator, and so forth. Unfortunately, by the time a virus or spyware is detected by the scanning software, some damage on the particular computer or loss of privacy may have already occurred.
  • In some instances, malicious network content comprises a bot. A bot is a software robot configured to remotely control all or a portion of a digital device (e.g., a computer) without authorization by the digital device's legitimate owner. Bot related activities include bot propagation and attacking other computers on a network. Bots commonly propagate by scanning nodes (e.g., computers or other digital devices) available on a network to search for a vulnerable target. When a vulnerable computer is scanned, the bot may install a copy of itself. Once installed, the new bot may continue to seek other computers on a network to infect. A bot may also be propagated by a malicious web site configured to exploit vulnerable computers that visit its web pages.
  • A bot may also, without the authority of the infected computer user, establish a command and control communication channel to receive instructions. Bots may receive command and control communication from a centralized bot server or another infected computer (e.g., via a peer-to-peer (P2P) network established by a bot on the infected computer). When a plurality of bots (i.e., a botnet) act together, the infected computers (i.e., zombies) can perform organized attacks against one or more computers on a network, or engage in criminal enterprises. In one example, bot infected computers may be directed to flood another computer on a network with excessive traffic in a denial-of-service attack. In another example, upon receiving instructions, one or more bots may direct the infected computer to transmit spam across a network. In a third example, bots may host illegal businesses such as pharmaceutical websites that sell pharmaceuticals without a prescription.
  • Malicious network content may be distributed over a network via web sites, e.g., servers operating on a network according to an HTTP standard. Malicious network content distributed in this manner may be actively downloaded and installed on a user's computer, without the approval or knowledge of the user, simply by accessing the web site hosting the malicious network content. The web site hosting the malicious network content may be referred to as a malicious web site. The malicious network content may be embedded within data associated with web pages hosted by the malicious web site. For example, a web page may include JavaScript code, and malicious network content may be embedded within the JavaScript code. In this example, the malicious network content embedded within the JavaScript code may be obfuscated such that it is not apparent until the JavaScript code is executed that the JavaScript code contains malicious network content. Therefore, the malicious network content may attack or infect a user's computer before detection by antivirus software, firewalls, intrusion detection systems, or the like.
  • SUMMARY
  • A method for detecting malicious network content comprises inspecting one or more packets of network content, identifying a suspicious characteristic of the network content, determining a score related to a probability that the network content includes malicious network content based on at least the suspicious characteristic, identifying the network content as suspicious if the score satisfies a threshold value, executing a virtual machine to process the suspicious network content, and analyzing a response of the virtual machine to detect malicious network content.
  • BRIEF DESCRIPTION OF THE DRAWINGS
  • FIG. 1 is a diagram of an exemplary malicious network content detection environment 100.
  • FIG. 2 illustrates an exemplary analysis environment.
  • FIG. 3 illustrates an exemplary method for detecting malicious network content.
  • FIG. 4 illustrates another exemplary method for detecting malicious network content.
  • FIG. 5 illustrates an exemplary controller.
  • DESCRIPTION OF EXEMPLARY EMBODIMENTS
  • Network content may include any data transmitted over a network (i.e., network data). Network data may include text, software, images, audio, or other digital data. An example of network content includes web content, or any network data that may be transmitted using a Hypertext Transfer Protocol (HTTP), HyperText Markup Language (HTML) protocol, or be transmitted in a manner suitable for display on a web browser software application. Another examples of network content includes email messages, which may be transmitted using an email protocol such as Simple Mail Transfer Protocol (SMTP), Post Office Protocol version 3 (POP3), or Internet Message Access Protocol (IMAP4). A further example of network content includes Instant Messages, which may be transmitted using an Instant Messaging protocol such as Session Initiation Protocol (SIP) or Extensible Messaging and Presence Protocol (XMPP). In addition, network content may include any network data that is transferred using other data transfer protocols, such as File Transfer Protocol (FTP). We distinguish network content from network protocol header information used for addressing, routing, and otherwise delivering the network content.
  • To detect malicious network content (e.g., malicious web content) being transmitted over a communication network to a computing device, a virtual machine may be used to simulate the receipt and processing of network content on the receiving system. A determination may be made as to whether the network content is malicious based on a response of the virtual machine to the network content. Sometimes, suspicious network content is determined to be non-malicious. Processing the suspicious network content in a virtual machine is an important step to determine whether the suspicious network content is in fact malicious and prevent a false assumption that the suspicious network content is malicious. False positives in detecting malicious network content may be avoided by processing suspicious network content in a virtual machine and detecting malicious network content by analyzing the virtual machine's response to the suspicious network content.
  • In the prior art, a proxy may be used in the network between the computing device and a web server hosting the malicious network content. The proxy may intercept a request for network content issued by a web browser executing on the computing device. The proxy may then issue the request to the web server as a proxy on behalf of the computing device. The proxy may receive a response to the request from the web server. The proxy may then process a data exchange including the request and response on a virtual machine and evaluate the virtual machine's response to the data exchange to detect malicious network content. If no malicious network content is detected, the proxy may forward the requested network content to the computing device from which the original request originated.
  • Because each data exchange is processed using a virtual machine, this approach is highly computation intensive, and is not scalable for large numbers of computing devices on a network. Also, because the requested network content is not delivered to the computing device until after it has been determined that the requested network content does not include malicious network content, a significant delay is introduced between the request for network content and the delivery of the requested network content.
  • Provos et al. (N. Provos, P. Mavrommatis, M. A. Rajab, and F. Monrose, “All your iFRAMEs Point to Us,” Google Technical Report provos-2008a, Feb. 4, 2008) reported on an analysis of web malware using a large web repository and corpus of malicious URLs. Provos et al. collected data for the analysis by first using a machine-learning framework in a pre-processing phase to extract features from web pages in the web repository and translate the features into a likelihood score. Next, a virtual machine was used in a verification phase to verify candidates identified by the machine-learning framework. Approximately 0.1% of the web pages in the web repository were processed by the virtual machine in the verification phase. Provos et al. noted that exhaustive inspection of each URL in the repository is prohibitively expensive. The system used by Provos et al. relied on a crawler proceeding gradually through the web to gather data in the repository for inspection, and could not inspect and select web pages in transit in the network for examination in a virtual machine.
  • FIG. 1 is a diagram of an exemplary malicious network content detection environment 100. The malicious network content detection environment 100 comprises a server device 105, a client device 110, and a tap 115, each coupled to a communication network 120. In various embodiments, there may be multiple server devices 105 and multiple client devices 110. The tap 115 is further coupled to a malicious network content detection system 125. The malicious network content detection system 125 may monitor exchanges of network content (e.g., web content) rather than intercepting and holding the network content until after determining whether the network content includes malicious network content. The malicious network content detection system 125 may be configured to inspect exchanges of network content over the communication network 120, identify suspicious network content, and analyze the suspicious network content using a virtual machine to detect malicious network content. In this way, the malicious network content detection system 125 may be computationally efficient and scalable as data traffic volume and a number of computing devices communicating over the communication network 120 increase. Therefore, the malicious network content detection system 125 may not become a bottleneck in the malicious network content detection environment 100.
  • The communication network 120 may include a public computer network such as the Internet, or a private computer network such as a wireless telecommunication network, wide area network, or local area network, or a combination of networks. Though the communication network 120 may include any type of network and be used to communicate different types of data, communications of web data may be discussed below for purposes of example.
  • The server device 105 and the client device 110 may include digital devices. Some examples of digital devices include computers, servers, laptops, personal digital assistants, and cellular telephones. The server device 105 may be configured to transmit network data over the communication network 120 to the client device 110. The client device 110 may be configured to receive the network data from the server device 105. The network data may include network content, such as web pages transmitted using a network communications protocol (e.g., Hypertext Transfer Protocol, or HTTP). In various embodiments, the server device 105 may include a web server configured to provide network content. The client device 110 may include a web browser configured to retrieve and/or display network content.
  • The tap 115 may include a digital data tap configured to monitor network data and provide a copy of the network data to the malicious network content detection system 125. Network data may comprise signals and data that are transmitted over the communication network 120 including data flows from the server device 105 to the client device 110. In one example, the tap 115 monitors and copies the network data without an appreciable decline in performance of the server device 105, the client device 110, or the communication network 120. The tap 115 may copy any portion of the network data. For example, the tap 115 may receive and copy any number of data packets from the network data.
  • In some embodiments, the network data may be organized into one or more data flows and provided to the malicious network content detection system 125. In various embodiments, the tap 115 may sample the network data based on a sampling scheme. Data flows may then be reconstructed based on the network data samples.
  • The tap 115 may also capture metadata from the network data. The metadata may be associated with the server device 105 and/or the client device 110. For example, the metadata may identify the server device 105 and/or the client device 110. In some embodiments, the server device 105 transmits metadata which is captured by the tap 115. In other embodiments, a heuristic module 130 (described herein) may determine the server device 105 and the client device 110 by analyzing data packets within the network data in order to generate the metadata.
  • The malicious network content detection system 125 may include a digital device, software, or a combination thereof that receives network data from the tap 115. The malicious network content detection system 125 includes a heuristic module 130, a heuristics database 135, a scheduler 140, a virtual machine pool 145, and an analysis environment 150. In some embodiments, the tap 115 may be contained within the malicious network content detection system 125.
  • The heuristic module 130 receives the copy of the network data from the tap 115 and applies heuristics to the data to determine if the network data might contain suspicious network content. The heuristics applied by the heuristic module 130 may be based on data and/or rules stored in the heuristics database 135. In one example, the heuristic module 130 flags network data as suspicious after applying a heuristic analysis. The network data may then be buffered and organized into a data flow. The data flow may then be provided to the scheduler 140. In some embodiments, the suspicious network data is provided directly to the scheduler 140 without buffering or organizing the data flow. In other embodiments, a notification of a group of data flows (e.g., a set of related web page requests and responses) may be sent to the scheduler 140 for later retrieval by the virtual machine.
  • The heuristic module 130 may perform one or more heuristic analyses on the network data. The heuristic module 130 may retain data packets belonging to a particular data flow previously copied by the tap 115. In one example, the heuristic module 130 receives data packets from the tap 115 and stores the data packets within a buffer or other memory. Once the heuristic module 130 receives a predetermined number of data packets from a particular data flow, the heuristic module 130 performs the heuristics and/or probability analysis.
  • In some embodiments, the heuristic module 130 performs a heuristic analysis on a set of data packets belonging to a data flow and then stores the data packets within a buffer or other memory. The heuristic module 130 may then continue to receive new data packets belonging to the same data flow. Once a predetermined number of new data packets belonging to the same data flow are received, the heuristic analysis may be performed upon the combination of buffered and new data packets to determine a likelihood of suspicious network content.
  • In some embodiments, an optional buffer receives the flagged network data from the heuristic module 130. The buffer may be used to store and organize the flagged network data into one or more data flows before providing the one or more data flows to the scheduler 140. In various embodiments, the buffer is used to store network data until the network data is provided to the scheduler 140. In one example, the buffer stores the network data to allow other components of the malicious network content detection system 125 time to complete functions or otherwise clear data congestion.
  • In some embodiments, the heuristic module 130 may maintain copies of network content data of potential interest to virtual machines and provide the network content data on request (e.g., when a web browser later executes inside a virtual machine and requests entities that were transmitted on the network earlier). The length of time that the heuristic module 130 keeps this data in memory may be based on how suspicious the data is, how much workload the system is under, and/or other factors.
  • The scheduler 140 may identify the client device 110 and retrieve a virtual machine associated with the client device 110. A virtual machine is software that is configured to mimic the performance of a device (e.g., the client device 110). The virtual machine may be retrieved from the virtual machine pool 145. Furthermore, the scheduler 140 may identify a web browser running on the client device 110, and retrieve a virtual machine associated with the web browser.
  • In some embodiments, the heuristic module 130 transmits the metadata identifying the client device 110 to the scheduler 140. In other embodiments, the scheduler 140 receives one or more data packets of the network data from the heuristic module 130 and analyzes the one or more data packets to identify the client device 110. In yet other embodiments, the metadata may be received from the tap 115.
  • The scheduler 140 may retrieve and configure the virtual machine to mimic the pertinent performance characteristics of the client device 110. In one example, the scheduler 140 configures the characteristics of the virtual machine to mimic only those features of the client device 110 that are affected by the network data copied by the tap 115. The scheduler 140 may determine the features of the client device 110 that are affected by the network data by receiving and analyzing the network data from the tap 115. Such features of the client device 110 may include ports that are to receive the network data, select device drivers that are to respond to the network data, and any other devices coupled to or contained within the client device 110 that can respond to the network data. In other embodiments, the heuristic module 130 may determine the features of the client device 110 that are affected by the network data by receiving and analyzing the network data from the tap 115. The heuristic module 130 may then transmit the features of the client device to the scheduler 140.
  • The virtual machine pool 145 may be configured to store one or more virtual machines. The virtual machine pool 145 may include software and/or a storage medium capable of storing software. In one example, the virtual machine pool 145 stores a single virtual machine that can be configured by the scheduler 140 to mimic the performance of any client device 110 on the communication network 120. The virtual machine pool 145 may store any number of distinct virtual machines that can be configured to simulate the performance of a wide variety of client devices 110.
  • The analysis environment 150 simulates the receipt and/or display of the network content from the server device 105 after the network content is received by the client device 110 to analyze the effects of the network content upon the client device 110. The analysis environment 150 may identify the effects of malware or malicious network content by analyzing the simulation of the effects of the network content upon the client device 110 that is carried out on the virtual machine. There may be multiple analysis environments 150 to simulate multiple streams of network content. The analysis environment 150 is further discussed with respect to FIG. 2.
  • Although FIG. 1 depicts data transmitted from the server device 105 to the client device 110, either device can transmit and receive data from the other. Similarly, although only two devices are depicted, any number of devices can send and/or receive data across the communication network 120. Moreover, the tap 115 can monitor and copy data transmitted from multiple devices without appreciably effecting the performance of the communication network 120 or the devices coupled to the communication network 120.
  • FIG. 2 illustrates an exemplary analysis environment. The analysis environment 150 includes a replayer 205, a virtual switch 210, and a virtual machine 215. The replayer 205 receives network content that has been flagged by the heuristic module 130 and provides the network content to the virtual machine 215 via the virtual switch 210 (i.e., replays the network content) in the analysis environment 150. In some embodiments, the replayer 205 mimics the behavior of the server device 105 in transmitting the flagged network content. There may be any number of replayers 205 simulating the transmission of network content between the server device 105 and the client device 110. In a further embodiment, the replayer 205 dynamically modifies session variables, as is appropriate, to emulate a “live” client or server of the protocol sequence being replayed. In one example, dynamic variables that may be dynamically substituted include dynamically assigned ports, transaction IDs, and any other variable that is dynamic to each protocol session.
  • The virtual switch 210 may include software that is capable of forwarding packets of flagged network content to the virtual machine 215. In one example, the replayer 205 simulates the transmission of the data flow by the server device 105. The virtual switch 210 simulates the communication network 120, and the virtual machine 215 simulates the client device 110. The virtual switch 210 may route the data packets of the data flow to the correct ports of the virtual machine 215.
  • In some embodiments, requests for data from client software in the virtual machine 215 (e.g., a web browser) may be proxied by the replayer to the heuristic module 130 where the data has been cached, and a response from the heuristic module 130 may then be proxied back to the client software executing in the virtual machine 215.
  • The virtual machine 215 includes a representation of the client device 110 that may be provided to the analysis environment 150 by the scheduler 140. In one example, the scheduler 140 retrieves an instance of the virtual machine 215 from the virtual machine pool 145 and configures the virtual machine 215 to mimic a client device 110. The configured virtual machine 215 is then provided to the analysis environment 150 where it may receive flagged network content from the virtual switch 210.
  • As the analysis environment 150 simulates the transmission and reception of the network content, behavior of the virtual machine 215 can be closely monitored for unauthorized activity. If the virtual machine 215 crashes, performs illegal operations, performs abnormally, or allows access of data to an unauthorized entity (e.g., an unauthorized computer user, a bot, etc.), the analysis environment 150 may react. In one example, the analysis environment 150 may transmit a command to the client device 110 to stop accepting the network content or data flows from the server device 105.
  • In some embodiments, the analysis environment 150 monitors and analyzes the behavior of the virtual machine 215 in order to determine a specific type of malware or malicious network content. The analysis environment 150 may also generate computer code configured to eliminate new viruses, worms, bots, adware, spyware, or other malware or malicious network content. In various embodiments, the analysis environment 150 generates computer code configured to repair damage performed by malware or malicious network content. By simulating the transmission and reception of suspicious network content and analyzing the response of the virtual machine 215, the analysis environment 150 may identify known and previously unidentified malware and malicious network content before a computer system is damaged or compromised.
  • FIG. 3 illustrates an exemplary method 300 for detecting malicious network content. In step 305, a packet of network content is intercepted or copied. The packet may be intercepted and/or copied from a network data transmission between the server device 105 and an intended destination (e.g., the client device 110), such as by the tap 115. Alternatively, the packet may be intercepted and/or copied from a network data transmission between the client device 110 and an intended destination (e.g., the server device 105). The packet may include a request for data, such as network content, or data provided in response to a request.
  • In step 310, a packet of network content is inspected. The heuristic module 130 may utilize one or more heuristics to inspect the packet of network content for suspicious network content which indicates the potential presence of malicious network content or malware within the packet.
  • A packet of network content may be part of a data flow which includes additional packets of network content. For example, the packet of network content may represent a portion of a web page, while other related packets in the data flow represent additional portions of the web page. The packet of network content may be stored along with the other related packets of network content comprising the data flow, such that multiple packets of network content within the data flow may be inspected in a sequence or in parallel. The malicious network content detection system may store the packets of network content and all or a portion of a data flow. The data flow and data packets may be stored for any length of time, from a few seconds to minutes, tens of minutes, or more, for analysis at any time.
  • To facilitate longer storage times for data flows over a high data rate communication network, large data objects comprised of numerous data packets may be truncated to a small subset of representative data packets. Data object truncation is particularly useful where network communication bandwidth is mostly utilized by a small percentage of large data objects, such as video. For example, video data may be truncated to a few data packets, such as the first few data packets. An extent to which the large data objects are truncated may be adaptive based on available memory, data bandwidth, type of data objects, and other factors. An amount of memory allocated to storing a data flow may also be dependent upon a characteristic of the data flow, such as data type. In an example, octet streams, text streams, HTML streams, and miscellaneous binary streams may be allocated 1 megabyte (MB). Images and PDF files may be allocated 384 kilobytes (kB). Video, audio, and most other data types may be allocated 128 kB. The memory allocated to storing each data flow type may be adjusted, periodically or dynamically, to improve analysis throughput while maintaining accuracy in detection of malicious network content and working within memory limitations.
  • In step 315, a suspicious characteristic of the network content is identified. The heuristic module 130 may identify the suspicious characteristic of the network content as a result of inspecting the network content in step 310. When a characteristic of the packet, such as a sequence of characters or keyword, is identified that meets the conditions of a heuristic used in step 310, a suspicious characteristic or “feature” of the network content is identified. The identified features may be stored for reference and analysis. In some embodiments, the entire packet may be inspected and multiple features may be identified before proceeding to the next step. In some embodiments, features may be determined as a result of an analysis across multiple packets comprising the network content.
  • Keywords used by heuristics may be chosen by performing an approximate Bayesian probability analysis of all the keywords in an HTML specification using a corpus of malicious network content and a corpus of non-malicious network content. The approximate Bayesian probability analysis may be based on the principles of the Bayesian theorem and/or naïve Bayesian classification. For instance, a probability Pm that the keyword appears in malicious network content may be computed using the corpus of malicious network content, while a probability Pn that the keyword appears in non-malicious network content may be computed using the corpus of non-malicious network content. A given keyword may be determined to be a suspicious characteristic for being associated with malicious network content if a score based on a computed ratio Pm/Pn exceeds a threshold of suspicion. The threshold of suspicion may be a value greater than 1, 10, 30, 60, 100, or some other number indicating how much more likely the suspicious characteristic is to indicate malicious network content than to indicate non-malicious network content.
  • In step 320, a score related to a probability that the suspicious characteristic identified in step 315 indicates malicious network content is determined. An approximate Bayesian probability analysis may be used to determine the score. In various embodiments, the approximate Bayesian probability analysis may be performed in real-time or using a look-up table based on a previously performed approximate Bayesian probability analysis.
  • For example, the approximate Bayesian probability analysis may be performed to determine a relative probability score that a particular feature is associated with the presence of malicious network content in a packet by comparing a corpus of malicious network content and a corpus of regular, non-malicious network content. A feature may include a characteristic of the packet, such as a sequence of characters or keyword, that meets the conditions of a heuristic used in step 310. The feature may also include a characteristic involving more than one packet inspected in sequence or in parallel. An example of a feature may include the character sequence “eval(unescape(”, which indicates a JavaScript “unescape” command nested within a JavaScript “eval” command argument. Further examples of features are described below with respect to step 445 in method 400. A probability Pflm that the feature is present in a packet of malicious network content is computed by analyzing the corpus of malicious network content. A probability Pfln that the feature is present in a packet of non-malicious network content is computed by analyzing the corpus of non-malicious network content. A malicious probability score is computed as the base two logarithm of a relative probability factor Pmlf that the feature is associated with malicious network content. The malicious probability score is computed by computing the ratio of the base two logarithm (log2) of the probability that the feature is present in a packet of malicious network content and the base two logarithm of the probability that the feature is present in a packet of non-malicious network content. The relative probability factor Pmlf may be expressed as follows:

  • log2(P mlf)=log2(P flm)/log2(P fln)  Equation 1
  • The size of the result log2(Pmlf) (i.e., malicious probability score) may indicate the probability that the suspicious network content includes malicious network content. For example, a result of eleven may indicate that the feature is approximately two thousand times more likely to appear in malicious network content than in non-malicious network content. Likewise, a value of twelve may indicate that the feature is approximately four thousand times more likely to appear in malicious network content.
  • In some embodiments, the malicious corpus and/or the non-malicious corpus may be continuously updated in response to monitored network data traffic, and the malicious probability scores associated with the features may be continuously updated in response to the updates to the corpuses. In other embodiments, the corpuses may be created and used in advance to store pre-computed malicious probability scores in a look-up table for reference when features are identified. The features associated with significant probabilities of malicious network content may change as the corpuses change.
  • In step 325, malicious network content is identified or flagged if the malicious probability score of a feature computed in step 320 satisfies an analysis threshold. The analysis threshold may be greater than 1, 10, 30, 60, 100, 1000, 2000, or higher. The analysis threshold may be preset, or may be variable based on operating conditions of the malicious network content detection system 125. If the malicious probability score does not satisfy the analysis threshold, no action may be taken with regard to the feature associated with the malicious probability score. Otherwise, the analysis may proceed to the next step, such as step 330 for analysis through processing by a virtual machine, such as the virtual machine 215. In some embodiments, the malicious probability scores of all features computed in step 320 may be compared against the analysis threshold to assign a priority level to each feature and/or the packet as a whole. The priority level may be computed based on a variety of factors, such as the number of features identified in the packet, the highest malicious probability score of a feature in the packet, an average malicious probability score of the features in the packet, a mean malicious probability score of the features in the packet, and the like.
  • The analysis threshold may be adaptive or be frequently updated based on operating conditions of the malicious network content detection system 125. For example, the threshold value may be dynamically revised according to a quantity of packets of network content to be inspected. As a quantity of data packets which are intercepted and/or copied from the network data transmission in step 310 increases, a quantity of data packets to be inspected may also increase. This may increase a computational load and leave less computational bandwidth available for more detailed analysis of the data packets. Consequently, the threshold may be increased to compensate for the decrease in available computational bandwidth for more detailed analysis. As another example, the threshold value may be dynamically revised according to an availability of one or more virtual machines to be used for the more detailed analysis. The threshold value may be set such that only features which have a significant probability of indicating malicious network content are processed using a virtual machine. For example, out of over one thousand features, less than fifty may be considered significant.
  • There may be multiple dynamically adaptive thresholds, which may be synchronized with each other. For example, the scheduler 140 may use a threshold to determine whether a virtual machine should be dispatched to process a queued suspicious network content. The scheduler 140's threshold may increase due to lack of available computational resources for the analysis environment 150 to execute virtual machines. The heuristic module 130 may use another threshold to determine whether heuristics should be applied to an identified feature. The heuristic module 130's threshold may be based on the malicious probability score for the identified feature. As the scheduler 140's threshold increases, the heuristic module 130's threshold may also increase. This is because flagging suspicious network content based on running heuristics on identified features may be irrelevant and an inefficient use of computational resources if the scheduler 140 will not process the suspicious network content in a virtual machine due to an increased threshold in the scheduler 140.
  • After suspicious network content has been flagged at step 325 for further analysis, the entire stored data flow including the suspicious network content may be reanalyzed. Each feature may be given a higher malicious probability score by virtue that one feature in the data flow has been found to have a malicious probability score greater than the threshold. A priority level for each feature found in the data flow may also be increased. Furthermore, all data packets and data flows associated with any domains associated with suspicious network content may be cached and given higher priorities and malicious probability scores than they would otherwise. The scheduler 140 may execute the virtual machine to process each flagged suspicious network content in the data flow individually, in priority order, in their original sequence of presentation, or in some other order. The virtual machine may process the suspicious network content until pre-empted by a higher priority suspicious network content.
  • In step 330, a virtual machine is executed to process the suspicious network content. The virtual machine may effectively replay the suspicious network content in a web browser executing on the virtual machine. The heuristic module 130 may provide the packet containing the suspicious network content to the scheduler 140, along with a list of the features present in the packet and the malicious probability scores associated with each of those features. Alternatively, the heuristic module 130 may provide a pointer to the packet containing the suspicious network content to the scheduler 140 such that the scheduler 140 may access the packet via a memory shared with the heuristic module 130. In another embodiment, the heuristic module 130 may provide identification information regarding the packet to the scheduler 140 such that the scheduler 140, replayer 205, or virtual machine may query the heuristic module 130 for data regarding the packet as needed.
  • The heuristic module 130 may also provide a priority level for the packet and/or the features present in the packet. The scheduler 140 may then load and configure a virtual machine from the virtual machine pool 145, and dispatch the virtual machine to the analysis environment 150 to process the suspicious network content. The virtual machine may be configured to execute for a minimum amount of processing, or for a minimum period of time, such as approximately 45 seconds. After the minimum period of time passes, the virtual machine may be pre-empted by the scheduler 140 to dispatch another virtual machine. Multiple virtual machines may be run simultaneously.
  • The scheduler 140 may choose which feature to process first according to the priority levels provided by the heuristic module 130. The scheduler 140 may cause another virtual machine already processing or analyzing another feature or packet, or set of packets, in the analysis environment 150 to terminate prior to dispatching the loaded virtual machine. For example, this may occur if computational resources are occupied with other virtual machines processing other features and therefore are not available to execute the loaded virtual machine. The scheduler 140 may choose which virtual machine(s) to terminate based on the priority levels of the features being processed by the virtual machine, how much time the virtual machine has already spent executing, or other reasons.
  • The scheduler 140 may reprioritize suspicious network content already in queue to be processed by virtual machines based on newly identified suspicious network content. For example, already queued suspicious network content may be reprioritized if there is a domain identified in common with the newly identified suspicious network content. Numerous incidents of suspicious network content associated with a single domain may increase the priority of all suspicious network content associated with the domain.
  • The replayer 205 in the analysis environment 150 may keep track of network content requested by the virtual machine. If suspicious network content already in the scheduler 140's queue is requested and processed by the virtual machine while processing other previously dispatched suspicious network content, and the queued suspicious network content is not found to be malicious, then the scheduler 140 may delete the queued suspicious network content from the queue. In this way, computational requirements can be reduced because an item of suspicious network content may only be processed in a virtual machine once, rather than each time a reference to the item of suspicious network content is made by another item of suspicious network content.
  • In step 335, malicious network content is detected by analyzing the virtual machine response to the suspicious network content. The analysis environment 150 may be configured to monitor the virtual machine for indications that the suspicious network content is in fact malicious network content. The analysis environment 150 may monitor the virtual machine for unusual memory accesses, unusual spawning of executable processes, unusual network transmissions, crashes, unusual changes in performance, and the like. The analysis environment may flag the suspicious network content as malicious network content according to the observed behavior of the virtual machine.
  • If a virtual machine processes suspicious network content for greater than a predetermined amount of time without any malicious network content being detected, the scheduler 140 may terminate the virtual machine to free up computational resources. The predetermined amount of time may be variable, according to a queue of suspicious network content that is awaiting processing by a virtual machine, the probability that the suspicious network content may be malicious network content, the feature being evaluated by the virtual machine, available computational resources, and the like. For example, the predetermined amount of time may be 45 seconds, two minutes, twenty minutes, or any other length of time.
  • If the suspicious network content is determined to be malicious network content, the malicious network content detection system 125 may report the malicious network content and/or log the malicious network content for future reference. For example, the malicious network content detection system 125 may generate an alert for a network content packet detected to include malicious network content. The malicious network content detection system 125 may report the malicious network content to an entity responsible for the client device 105. If the malicious network content was determined to originate from the server device 105, the client device 110 may be instructed not to continue network transmissions with the server device 105. If a party responsible for the server device 105 is known, the malicious network content detection system 125 may report the malicious network content to the party responsible for the server device 105. The server device 105 may be added to a list of malicious network content providers, and future network transmissions originating from the server device 105 may be blocked from reaching their intended destinations.
  • FIG. 4 illustrates another exemplary method 400 for detecting malicious network content. The method 400 may be performed by the heuristic module 130. In the method 400, a packet of network content is inspected to identify features which may indicate the presence of malicious network content. The method 400 may include the use of a single pass parser and/or an augmented finite state machine, which may maintain a stack of states. The method 400 may begin processing a data packet starting with a character after a character sequence “HTTP” has been identified.
  • In step 405, a data character is read from the data packet. The data character read may be subsequent to the character sequence “HTTP” or a data character previously read in a prior iteration of step 405. A pointer may be incremented to indicate the next data character to read in the method 400.
  • In step 410, the data character read in step 405 is evaluated to determine if the data character may indicate the start of a possible keyword or a possible feature as described with respect to method 300, or a different kind of data (e.g., JavaScript content embedded in HTML content). The data character may include a left angled bracket (i.e., “<”), for example. If the data character read may indicate the start of a keyword or a feature, the method may proceed to step 415. Otherwise, the method may proceed to step 420.
  • In step 415, a new state is pushed onto the stack of states to indicate that the method 400 has encountered the start of a keyword or feature. The new state may be an InKeyword state to indicate that the method is in the midst of processing a keyword. Depending on the character read, a different new state may be pushed onto the stack. A string of data characters may be stored, starting with the most recent character read or the next character to be read. The method 400 then proceeds to step 440.
  • In step 420, the data character read in step 405 is evaluated to determine if the data character may indicate the end of a keyword or a feature as described with respect to method 300. The data character may include a right angled bracket (i.e., “>”), for example. If the data character read may indicate the end of a keyword or a feature, the method may proceed to step 425. Otherwise, the method may proceed to step 440.
  • In step 425, heuristics to be applied to the data packet are identified and applied based on a character string read, which may start with the data character identified in step 410 and end with the data character identified in step 420. The heuristic module 300 may store the character string. The character string may be compared against a database of character strings stored in the heuristics database 135 to determine one or more heuristics that may be applied to the data packet based on the keyword. In some embodiments, a list of results of applying heuristics may be created. The list of results may be stored so that the list may be referenced in step 445.
  • Some examples of a heuristic that may be applied to the packet include keyword matches. Some keywords may be associated more with malicious network content than non-malicious network content, and their presence in a packet of network content may be an indication that the packet contains suspicious network content.
  • In one exemplary heuristic, an object filename's extension following a period may be examined. For example, a filename ending in the characters “.ini”, “.anr”, or “.htm” may be determined to be suspicious. Also, a filename generally associated with one filetype but associated with a different filetype in the reference may be determined to be suspicious. For example, a filename ending in “.jpg” which is not referring to an image file may be determined to be suspicious.
  • In other exemplary heuristics, content of web pages may be analyzed to determine whether network content is suspicious. For example, presence of small iframes, such as an iframe in which the width and/or height is 0 or 1 pixel, in a web page may be determined to be suspicious.
  • Further examples of heuristics may be associated with JavaScript code sequences. When an “eval(unescape( . . . ))” JavaScript command sequence, which includes an “unescape” command nested within the argument of an “eval” command, is detected in the data packet, the heuristic may evaluate the command sequence to identify suspicious network content. The “eval(unescape( . . . ))” command sequence may be used to obfuscate malicious network content so that the malicious network content is not easily detected in the network data transmission, and may therefore indicate suspicious network content.
  • Another example of a heuristic is a length of the argument of the “unescape” or other JavaScript function from a starting character to an ending character. The length may be determined by counting a number of characters, or measuring a length of time, between the opening parenthesis and the closing parenthesis after “unescape” or other function name. A greater number of characters between the parentheses may indicate that an obfuscated body to the command is being used.
  • Bi-gram detection is another exemplary heuristic that may be employed in JavaScript or other types of network content. In bi-gram detection, character transitions within the network content are analyzed. A table of conditional probabilities may be generated and updated continuously as data is evaluated. The table of conditional probabilities indicates the probability of each second character appearing after each first character. The conditional probability of a second character C2 given the first character C1 may be written as P(C2|C1). The heuristic may identify when a string of unusual character transitions occurs according to the table of conditional probabilities. Thresholds for the length of the string of unusual character transitions, combined with the values of the conditional probabilities that flags the character transitions as being unusual, may be set a priori based on an approximate Bayesian probability analysis using a corpus of malicious network content and a corpus of non-malicious network content. Alternatively, the thresholds may be adjusted in near real time as the table of conditional probabilities is updated. For example, a long string of unusual character transitions may indicate the presence of malicious network content in a JavaScript “eval(unescape( . . . ))” clause.
  • The use of domain profiles is another exemplary heuristic that may be used to reduce a rate of false positives from other heuristics. The domain profiles heuristic may be used in conjunction with other heuristics in order to increase throughput and reduce computational requirements for detecting malicious network content. Each network domain with which monitored network content is exchanged may be cataloged and annotated with a list of the features present in network content associated with the network domain. A typical network domain may be approximately constant in the features present in associated network content. When a feature is identified by another heuristic, the feature may be looked up in the list of features associated with the network domain. If the feature is listed as being associated with the network domain, and malicious network content was not previously detected due to identification of the feature in network content associated with the domain, a virtual machine may not be executed to process the network content containing the feature associated with the network domain. If, on the other hand, the feature was not previously detected or associated with the network domain, the network content may be identified as being suspicious and processed by a virtual machine.
  • A list of domains or web sites containing malicious network content may be maintained. The list of sources of malicious network content may be hosted on the computer network and accessible by clients on the computer network. The heuristic module 130 may access the list of domains and web sites containing malicious network content to supplement the information provided by the domain profiles heuristic. For example, the threshold for network content associated with a web site on a list of malicious network content sources may be set to be lower and/or the priority of a suspicious network content may be set higher than for other network content. When malicious network content is detected, the list of domains may be notified or updated with the information for reference by others.
  • In step 430, if a state is being exited, the state being exited is popped from the stack of states. The state being exited is the most recent state pushed onto the stack of states. For example, if the state being exited is the InKeyword state, the InKeyword state is popped from the stack of states to indicate that the method is no longer in the midst of reading a keyword. If a state is not being exited, a state may not be popped from the stack, and multiple states may be stored on the stack. In some embodiments, up to 32 states may be present on the stack of states at one time. For example, JavaScript may have embedded HTML, and therefore multiple states may be active at one time to account for nested features. In various embodiments, there may be more than 60 states associated with data packets being analyzed for malicious network content.
  • In step 435, a new state is pushed onto the stack of states to indicate that the method is now in the midst of a new state. The new state may be determined by the last keyword that was read, or a character indicating a new kind of content. For example, the new state may be an InBetweenKeyword state to indicate that the method is awaiting another keyword to process. In some embodiments, the new state may be an InJavaScript state to indicate that the method is in the midst of reading a JavaScript segment. The state may impact which heuristics are identified and applied to the packet of web data in step 445. For example, a first heuristic may be chosen if a first state is active, whereas a second heuristic may be chosen if a second state is active.
  • In step 440, the count of characters read in step 405 is evaluated to determine if the data character may lie at the end of a packet. If the data character lies at the end of the packet, the method may proceed to step 445. Otherwise, the method may proceed to step 405.
  • In step 445, the list of results produced by applying the heuristics in step 425 for the features in the data packet are referenced to determine which features in the data packet are to be processed using a virtual machine. Malicious probability scores for each feature may be compared against a threshold to determine whether the feature indicates suspicious network content. The features associated with the data packet may be ranked in priority order. The features may be used to prioritize whether to refer the data packet, and associated content, to a virtual machine in the order identified in step 425, in the priority order determined by their respective malicious probability scores, or in some other order.
  • FIG. 5 illustrates an exemplary controller 500. The controller 500 may comprise the malicious network content detection system 125 according to some embodiments. The controller 500 comprises at least a processor 505, a memory system 510, and a storage system 515, which are all coupled to a bus 520. The controller 500 may also comprise a communication network interface 525, an input/output (I/O) interface 530, and a display interface 535. The communication network interface 525 may couple with the communication network 120 via a communication medium 540. In some embodiments, the controller 500 may couple to a tap, such as the tap 115, which in turn couples with the communication network 120. The bus 520 provides communications between the communications network interface 525, the processor 505, the memory system 510, the storage system 515, the I/O interface 530, and the display interface 535.
  • The communications network interface 525 may communicate with other digital devices (not shown) via the communications medium 540. The processor 505 executes instructions. The memory system 510 permanently or temporarily stores data. Some examples of the memory system 510 are RAM and ROM. The storage system 515 also permanently or temporarily stores data. Some examples of the storage system 515 are hard disks and disk drives. The I/O interface 530 may include any device that can receive input and provide output to a user. The I/O interface 530 may include, but is not limited to, a keyboard, a mouse, a touchscreen, a keypad, a biosensor, a compact disc (CD) drive, a digital versatile disc (DVD) drive, or a floppy disk drive. The display interface 535 may include an interface configured to support a display, monitor, or screen. In some embodiments, the controller 500 comprises a graphical user interface to be displayed to a user over a monitor in order to allow the user to control the controller 500.
  • The embodiments discussed herein are illustrative. As these embodiments are described with reference to illustrations, various modifications or adaptations of the methods and/or specific structures described may become apparent to those skilled in the art.
  • The above-described modules may be comprised of instructions that are stored on storage media (e.g., computer readable media). The instructions may be retrieved and executed by a processor (e.g., the processor 600). Some examples of instructions include software, program code, and firmware. Some examples of storage media comprise memory devices and integrated circuits. The instructions are operational when executed by the processor to direct the processor to operate in accordance with embodiments of the present invention. Those skilled in the art are familiar with instructions, processor(s), and storage media.
  • In the foregoing specification, the invention is described with reference to specific embodiments thereof, but those skilled in the art will recognize that the invention is not limited thereto. Various features and aspects of the above-described invention can be used individually or jointly. Further, the invention can be utilized in any number of environments and applications beyond those described herein without departing from the broader spirit and scope of the specification. The specification and drawings are, accordingly, to be regarded as illustrative rather than restrictive. It will be recognized that the terms “comprising,” “including,” and “having,” as used herein, are specifically intended to be read as open-ended terms of art.

Claims (1)

What is claimed is:
1. A method for detecting malicious network content, comprising:
inspecting one or more packets of network content;
identifying a suspicious characteristic of the network content;
determining a score related to a probability that the network content includes malicious network content based on at least the suspicious characteristic;
identifying the network content as suspicious if the score satisfies a threshold value;
executing a virtual machine to process the suspicious network content; and
analyzing a response of the virtual machine to detect malicious network content.
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Cited By (32)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US9438613B1 (en) 2015-03-30 2016-09-06 Fireeye, Inc. Dynamic content activation for automated analysis of embedded objects
US9497213B2 (en) 2013-03-15 2016-11-15 Fireeye, Inc. System and method to manage sinkholes
US9560059B1 (en) 2013-11-21 2017-01-31 Fireeye, Inc. System, apparatus and method for conducting on-the-fly decryption of encrypted objects for malware detection
US9609007B1 (en) 2014-08-22 2017-03-28 Fireeye, Inc. System and method of detecting delivery of malware based on indicators of compromise from different sources
US9641546B1 (en) 2013-03-14 2017-05-02 Fireeye, Inc. Electronic device for aggregation, correlation and consolidation of analysis attributes
US9661018B1 (en) 2004-04-01 2017-05-23 Fireeye, Inc. System and method for detecting anomalous behaviors using a virtual machine environment
US9661009B1 (en) 2014-06-26 2017-05-23 Fireeye, Inc. Network-based malware detection
US20170277554A1 (en) * 2016-03-25 2017-09-28 Intel Corporation Technologies for dynamically managing data bus bandwidth usage of virtual machines in a network device
US9787700B1 (en) 2014-03-28 2017-10-10 Fireeye, Inc. System and method for offloading packet processing and static analysis operations
US9792196B1 (en) 2013-02-23 2017-10-17 Fireeye, Inc. Framework for efficient security coverage of mobile software applications
US9825989B1 (en) 2015-09-30 2017-11-21 Fireeye, Inc. Cyber attack early warning system
US9824211B2 (en) 2013-03-15 2017-11-21 Fireeye, Inc. System and method to visualize user sessions
US9825976B1 (en) 2015-09-30 2017-11-21 Fireeye, Inc. Detection and classification of exploit kits
US9824216B1 (en) 2015-12-31 2017-11-21 Fireeye, Inc. Susceptible environment detection system
US9838411B1 (en) 2004-04-01 2017-12-05 Fireeye, Inc. Subscriber based protection system
US9846776B1 (en) 2015-03-31 2017-12-19 Fireeye, Inc. System and method for detecting file altering behaviors pertaining to a malicious attack
US9912644B2 (en) 2014-08-05 2018-03-06 Fireeye, Inc. System and method to communicate sensitive information via one or more untrusted intermediate nodes with resilience to disconnected network topology
US9912698B1 (en) 2013-03-13 2018-03-06 Fireeye, Inc. Malicious content analysis using simulated user interaction without user involvement
US9934381B1 (en) 2013-03-13 2018-04-03 Fireeye, Inc. System and method for detecting malicious activity based on at least one environmental property
US10025927B1 (en) 2013-03-13 2018-07-17 Fireeye, Inc. Malicious content analysis with multi-version application support within single operating environment
US10033759B1 (en) 2015-09-28 2018-07-24 Fireeye, Inc. System and method of threat detection under hypervisor control
US10075455B2 (en) 2014-12-26 2018-09-11 Fireeye, Inc. Zero-day rotating guest image profile
US10083302B1 (en) 2013-06-24 2018-09-25 Fireeye, Inc. System and method for detecting time-bomb malware
US10104102B1 (en) 2015-04-13 2018-10-16 Fireeye, Inc. Analytic-based security with learning adaptability
US10133866B1 (en) 2015-12-30 2018-11-20 Fireeye, Inc. System and method for triggering analysis of an object for malware in response to modification of that object
US10169585B1 (en) 2016-06-22 2019-01-01 Fireeye, Inc. System and methods for advanced malware detection through placement of transition events
US10176321B2 (en) 2015-09-22 2019-01-08 Fireeye, Inc. Leveraging behavior-based rules for malware family classification
US10200384B1 (en) 2013-03-14 2019-02-05 Fireeye, Inc. Distributed systems and methods for automatically detecting unknown bots and botnets
US10210329B1 (en) 2015-09-30 2019-02-19 Fireeye, Inc. Method to detect application execution hijacking using memory protection
US10216927B1 (en) 2015-06-30 2019-02-26 Fireeye, Inc. System and method for protecting memory pages associated with a process using a virtualization layer
US10284575B2 (en) 2015-11-10 2019-05-07 Fireeye, Inc. Launcher for setting analysis environment variations for malware detection
US10341363B1 (en) 2015-12-28 2019-07-02 Fireeye, Inc. Dynamically remote tuning of a malware content detection system

Families Citing this family (143)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US8898788B1 (en) 2004-04-01 2014-11-25 Fireeye, Inc. Systems and methods for malware attack prevention
US9027135B1 (en) 2004-04-01 2015-05-05 Fireeye, Inc. Prospective client identification using malware attack detection
US8881282B1 (en) 2004-04-01 2014-11-04 Fireeye, Inc. Systems and methods for malware attack detection and identification
US8561177B1 (en) 2004-04-01 2013-10-15 Fireeye, Inc. Systems and methods for detecting communication channels of bots
US8204984B1 (en) 2004-04-01 2012-06-19 Fireeye, Inc. Systems and methods for detecting encrypted bot command and control communication channels
US8566946B1 (en) 2006-04-20 2013-10-22 Fireeye, Inc. Malware containment on connection
US8375444B2 (en) 2006-04-20 2013-02-12 Fireeye, Inc. Dynamic signature creation and enforcement
US8584239B2 (en) 2004-04-01 2013-11-12 Fireeye, Inc. Virtual machine with dynamic data flow analysis
US8539582B1 (en) 2004-04-01 2013-09-17 Fireeye, Inc. Malware containment and security analysis on connection
US8793787B2 (en) 2004-04-01 2014-07-29 Fireeye, Inc. Detecting malicious network content using virtual environment components
US9106694B2 (en) 2004-04-01 2015-08-11 Fireeye, Inc. Electronic message analysis for malware detection
US8549638B2 (en) * 2004-06-14 2013-10-01 Fireeye, Inc. System and method of containing computer worms
US8009566B2 (en) 2006-06-26 2011-08-30 Palo Alto Networks, Inc. Packet classification in a network security device
US8856782B2 (en) 2007-03-01 2014-10-07 George Mason Research Foundation, Inc. On-demand disposable virtual work system
US7587537B1 (en) 2007-11-30 2009-09-08 Altera Corporation Serializer-deserializer circuits formed from input-output circuit registers
US8289884B1 (en) * 2008-01-14 2012-10-16 Dulles Research LLC System and method for identification of unknown illicit networks
US9098698B2 (en) 2008-09-12 2015-08-04 George Mason Research Foundation, Inc. Methods and apparatus for application isolation
US8850571B2 (en) 2008-11-03 2014-09-30 Fireeye, Inc. Systems and methods for detecting malicious network content
US8997219B2 (en) 2008-11-03 2015-03-31 Fireeye, Inc. Systems and methods for detecting malicious PDF network content
US9405931B2 (en) * 2008-11-14 2016-08-02 Dell Products L.P. Protected information stream allocation using a virtualized platform
US9177145B2 (en) * 2009-03-24 2015-11-03 Sophos Limited Modified file tracking on virtual machines
US8516590B1 (en) * 2009-04-25 2013-08-20 Dasient, Inc. Malicious advertisement detection and remediation
US8555391B1 (en) 2009-04-25 2013-10-08 Dasient, Inc. Adaptive scanning
US8683584B1 (en) 2009-04-25 2014-03-25 Dasient, Inc. Risk assessment
US9154364B1 (en) 2009-04-25 2015-10-06 Dasient, Inc. Monitoring for problems and detecting malware
US9348977B1 (en) * 2009-05-26 2016-05-24 Amazon Technologies, Inc. Detecting malware in content items
US8839422B2 (en) 2009-06-30 2014-09-16 George Mason Research Foundation, Inc. Virtual browsing environment
US20110035802A1 (en) * 2009-08-07 2011-02-10 Microsoft Corporation Representing virtual object priority based on relationships
US8832829B2 (en) * 2009-09-30 2014-09-09 Fireeye, Inc. Network-based binary file extraction and analysis for malware detection
US8489534B2 (en) 2009-12-15 2013-07-16 Paul D. Dlugosch Adaptive content inspection
US8266243B1 (en) * 2010-03-30 2012-09-11 Amazon Technologies, Inc. Feedback mechanisms providing contextual information
US10095530B1 (en) 2010-05-28 2018-10-09 Bromium, Inc. Transferring control of potentially malicious bit sets to secure micro-virtual machine
US8010992B1 (en) * 2010-07-14 2011-08-30 Domanicom Corp. Devices, systems, and methods for providing increased security when multiplexing one or more services at a customer premises
US9270690B2 (en) 2010-07-21 2016-02-23 Seculert Ltd. Network protection system and method
US20120077528A1 (en) * 2010-09-29 2012-03-29 Research In Motion Limited Method To Exchange Application Specific Information For Outgoing Calls Between Two Mobile Devices
US9032521B2 (en) * 2010-10-13 2015-05-12 International Business Machines Corporation Adaptive cyber-security analytics
US8429744B1 (en) * 2010-12-15 2013-04-23 Symantec Corporation Systems and methods for detecting malformed arguments in a function by hooking a generic object
CN102567304B (en) * 2010-12-24 2014-02-26 北大方正集团有限公司 Filtering method and device for network malicious information
US8479276B1 (en) * 2010-12-29 2013-07-02 Emc Corporation Malware detection using risk analysis based on file system and network activity
US8918785B1 (en) * 2010-12-29 2014-12-23 Amazon Technologies, Inc. Managing virtual machine network through security assessment
US8745734B1 (en) * 2010-12-29 2014-06-03 Amazon Technologies, Inc. Managing virtual computing testing
US8789186B2 (en) 2011-03-03 2014-07-22 Jpmorgan Chase Bank, N.A. System and method for packet profiling
US20120272317A1 (en) * 2011-04-25 2012-10-25 Raytheon Bbn Technologies Corp System and method for detecting infectious web content
US9047441B2 (en) * 2011-05-24 2015-06-02 Palo Alto Networks, Inc. Malware analysis system
US8695096B1 (en) * 2011-05-24 2014-04-08 Palo Alto Networks, Inc. Automatic signature generation for malicious PDF files
DE102011082237B4 (en) * 2011-09-07 2013-04-04 Deutsche Telekom Ag Network communication device for communication via a communication network
US8904524B1 (en) * 2011-09-27 2014-12-02 Emc Corporation Detection of fast flux networks
US9519781B2 (en) * 2011-11-03 2016-12-13 Cyphort Inc. Systems and methods for virtualization and emulation assisted malware detection
US9686293B2 (en) 2011-11-03 2017-06-20 Cyphort Inc. Systems and methods for malware detection and mitigation
US10095866B2 (en) 2014-02-24 2018-10-09 Cyphort Inc. System and method for threat risk scoring of security threats
US9792430B2 (en) 2011-11-03 2017-10-17 Cyphort Inc. Systems and methods for virtualized malware detection
US10326778B2 (en) 2014-02-24 2019-06-18 Cyphort Inc. System and method for detecting lateral movement and data exfiltration
WO2013082437A1 (en) 2011-12-02 2013-06-06 Invincia, Inc. Methods and apparatus for control and detection of malicious content using a sandbox environment
US9367687B1 (en) * 2011-12-22 2016-06-14 Emc Corporation Method for malware detection using deep inspection and data discovery agents
US9519782B2 (en) 2012-02-24 2016-12-13 Fireeye, Inc. Detecting malicious network content
US9317337B2 (en) * 2012-04-13 2016-04-19 International Business Machines Corporation Utilizing software component metadata to provision virtual machines in a networked computing environment
US9798588B1 (en) * 2012-04-25 2017-10-24 Significs And Elements, Llc Efficient packet forwarding using cyber-security aware policies
US9270689B1 (en) * 2012-06-21 2016-02-23 Cisco Technology, Inc. Dynamic and adaptive traffic scanning
US9922192B1 (en) * 2012-12-07 2018-03-20 Bromium, Inc. Micro-virtual machine forensics and detection
CN103632084A (en) * 2012-08-20 2014-03-12 百度在线网络技术(北京)有限公司 Building method for malicious feature data base, malicious object detecting method and device of malicious feature data base
CN103810424B (en) * 2012-11-05 2017-02-08 腾讯科技(深圳)有限公司 An identification method and apparatus of the abnormal applications
US9165142B1 (en) * 2013-01-30 2015-10-20 Palo Alto Networks, Inc. Malware family identification using profile signatures
US9824209B1 (en) 2013-02-23 2017-11-21 Fireeye, Inc. Framework for efficient security coverage of mobile software applications that is usable to harden in the field code
US9159035B1 (en) 2013-02-23 2015-10-13 Fireeye, Inc. Framework for computer application analysis of sensitive information tracking
US9009822B1 (en) 2013-02-23 2015-04-14 Fireeye, Inc. Framework for multi-phase analysis of mobile applications
US8990944B1 (en) 2013-02-23 2015-03-24 Fireeye, Inc. Systems and methods for automatically detecting backdoors
US9195829B1 (en) 2013-02-23 2015-11-24 Fireeye, Inc. User interface with real-time visual playback along with synchronous textual analysis log display and event/time index for anomalous behavior detection in applications
US9367681B1 (en) 2013-02-23 2016-06-14 Fireeye, Inc. Framework for efficient security coverage of mobile software applications using symbolic execution to reach regions of interest within an application
US9009823B1 (en) 2013-02-23 2015-04-14 Fireeye, Inc. Framework for efficient security coverage of mobile software applications installed on mobile devices
US9355247B1 (en) 2013-03-13 2016-05-31 Fireeye, Inc. File extraction from memory dump for malicious content analysis
US9521113B2 (en) * 2013-03-14 2016-12-13 Mcafee, Inc. Self-configuring local area network security
US9251343B1 (en) 2013-03-15 2016-02-02 Fireeye, Inc. Detecting bootkits resident on compromised computers
US9043922B1 (en) * 2013-04-19 2015-05-26 Symantec Corporation Systems and methods for determining malicious-attack exposure levels based on field-data analysis
US9495180B2 (en) 2013-05-10 2016-11-15 Fireeye, Inc. Optimized resource allocation for virtual machines within a malware content detection system
US9635039B1 (en) 2013-05-13 2017-04-25 Fireeye, Inc. Classifying sets of malicious indicators for detecting command and control communications associated with malware
US10133863B2 (en) 2013-06-24 2018-11-20 Fireeye, Inc. Zero-day discovery system
US8943594B1 (en) 2013-06-24 2015-01-27 Haystack Security LLC Cyber attack disruption through multiple detonations of received payloads
US9888016B1 (en) 2013-06-28 2018-02-06 Fireeye, Inc. System and method for detecting phishing using password prediction
US9300686B2 (en) 2013-06-28 2016-03-29 Fireeye, Inc. System and method for detecting malicious links in electronic messages
US9294501B2 (en) 2013-09-30 2016-03-22 Fireeye, Inc. Fuzzy hash of behavioral results
US10089461B1 (en) 2013-09-30 2018-10-02 Fireeye, Inc. Page replacement code injection
US9736179B2 (en) 2013-09-30 2017-08-15 Fireeye, Inc. System, apparatus and method for using malware analysis results to drive adaptive instrumentation of virtual machines to improve exploit detection
US9628507B2 (en) 2013-09-30 2017-04-18 Fireeye, Inc. Advanced persistent threat (APT) detection center
US9171160B2 (en) 2013-09-30 2015-10-27 Fireeye, Inc. Dynamically adaptive framework and method for classifying malware using intelligent static, emulation, and dynamic analyses
US10192052B1 (en) 2013-09-30 2019-01-29 Fireeye, Inc. System, apparatus and method for classifying a file as malicious using static scanning
US9690936B1 (en) 2013-09-30 2017-06-27 Fireeye, Inc. Multistage system and method for analyzing obfuscated content for malware
US9288220B2 (en) * 2013-11-07 2016-03-15 Cyberpoint International Llc Methods and systems for malware detection
US9921978B1 (en) 2013-11-08 2018-03-20 Fireeye, Inc. System and method for enhanced security of storage devices
US9148441B1 (en) * 2013-12-23 2015-09-29 Symantec Corporation Systems and methods for adjusting suspiciousness scores in event-correlation graphs
US9747446B1 (en) 2013-12-26 2017-08-29 Fireeye, Inc. System and method for run-time object classification
US9756074B2 (en) 2013-12-26 2017-09-05 Fireeye, Inc. System and method for IPS and VM-based detection of suspicious objects
EP3087527A4 (en) * 2013-12-27 2017-07-19 McAfee, Inc. System and method of detecting malicious multimedia files
US9292675B2 (en) * 2014-01-10 2016-03-22 SparkCognition, Inc. System and method for creating a core cognitive fingerprint
US20150213065A1 (en) * 2014-01-27 2015-07-30 Thomson Reuters Global Resources System and Methods for Cleansing Automated Robotic Traffic From Sets of Usage Logs
US9262635B2 (en) 2014-02-05 2016-02-16 Fireeye, Inc. Detection efficacy of virtual machine-based analysis with application specific events
US10110616B1 (en) * 2014-02-11 2018-10-23 DataVisor Inc. Using group analysis to determine suspicious accounts or activities
US10225280B2 (en) 2014-02-24 2019-03-05 Cyphort Inc. System and method for verifying and detecting malware
US9241010B1 (en) 2014-03-20 2016-01-19 Fireeye, Inc. System and method for network behavior detection
US10242185B1 (en) 2014-03-21 2019-03-26 Fireeye, Inc. Dynamic guest image creation and rollback
US9223972B1 (en) 2014-03-31 2015-12-29 Fireeye, Inc. Dynamically remote tuning of a malware content detection system
US9432389B1 (en) 2014-03-31 2016-08-30 Fireeye, Inc. System, apparatus and method for detecting a malicious attack based on static analysis of a multi-flow object
US9438623B1 (en) 2014-06-06 2016-09-06 Fireeye, Inc. Computer exploit detection using heap spray pattern matching
US9973531B1 (en) 2014-06-06 2018-05-15 Fireeye, Inc. Shellcode detection
US9594912B1 (en) 2014-06-06 2017-03-14 Fireeye, Inc. Return-oriented programming detection
US9015814B1 (en) 2014-06-10 2015-04-21 Kaspersky Lab Zao System and methods for detecting harmful files of different formats
US10084813B2 (en) 2014-06-24 2018-09-25 Fireeye, Inc. Intrusion prevention and remedy system
US9904781B2 (en) * 2014-07-28 2018-02-27 Iboss, Inc. Emulating expected network communications to applications in a virtual machine environment
US9773112B1 (en) * 2014-09-29 2017-09-26 Fireeye, Inc. Exploit detection of malware and malware families
US10027689B1 (en) * 2014-09-29 2018-07-17 Fireeye, Inc. Interactive infection visualization for improved exploit detection and signature generation for malware and malware families
US10291628B2 (en) * 2014-11-07 2019-05-14 International Business Machines Corporation Cognitive detection of malicious documents
US9690933B1 (en) 2014-12-22 2017-06-27 Fireeye, Inc. Framework for classifying an object as malicious with machine learning for deploying updated predictive models
US9838417B1 (en) 2014-12-30 2017-12-05 Fireeye, Inc. Intelligent context aware user interaction for malware detection
CN104601568B (en) * 2015-01-13 2019-05-21 深信服科技股份有限公司 Virtualization security isolation method and device
US20160232353A1 (en) * 2015-02-09 2016-08-11 Qualcomm Incorporated Determining Model Protection Level On-Device based on Malware Detection in Similar Devices
US20180007075A1 (en) * 2015-02-12 2018-01-04 Hewlett Packard Enterprise Development Lp Monitoring dynamic device configuration protocol offers to determine anomaly
US10148693B2 (en) 2015-03-25 2018-12-04 Fireeye, Inc. Exploit detection system
US9690606B1 (en) 2015-03-25 2017-06-27 Fireeye, Inc. Selective system call monitoring
US9646159B2 (en) 2015-03-31 2017-05-09 Juniper Networks, Inc. Multi-file malware analysis
US9594904B1 (en) 2015-04-23 2017-03-14 Fireeye, Inc. Detecting malware based on reflection
US9961090B2 (en) 2015-06-18 2018-05-01 Bank Of America Corporation Message quarantine
WO2017030569A1 (en) * 2015-08-18 2017-02-23 Hewlett Packard Enterprise Development Lp Identifying randomly generated character strings
US10033747B1 (en) 2015-09-29 2018-07-24 Fireeye, Inc. System and method for detecting interpreter-based exploit attacks
US10116528B2 (en) 2015-10-02 2018-10-30 Keysight Technologies Singapore (Holdings) Ptd Ltd Direct network traffic monitoring within VM platforms in virtual processing environments
US10142212B2 (en) * 2015-10-26 2018-11-27 Keysight Technologies Singapore (Holdings) Pte Ltd On demand packet traffic monitoring for network packet communications within virtual processing environments
US9817974B1 (en) 2015-11-10 2017-11-14 Trend Micro Incorporated Anti-malware program with stalling code detection
AU2016369460A1 (en) * 2015-12-19 2018-06-07 Bitdefender Ipr Management Ltd Dual memory introspection for securing multiple network endpoints
US10050998B1 (en) 2015-12-30 2018-08-14 Fireeye, Inc. Malicious message analysis system
US10237287B1 (en) * 2016-02-11 2019-03-19 Awake Security, Inc. System and method for detecting a malicious activity in a computing environment
US10218733B1 (en) * 2016-02-11 2019-02-26 Awake Security, Inc. System and method for detecting a malicious activity in a computing environment
US10218717B1 (en) * 2016-02-11 2019-02-26 Awake Security, Inc. System and method for detecting a malicious activity in a computing environment
US9917855B1 (en) * 2016-03-03 2018-03-13 Trend Micro Incorporated Mixed analysys-based virtual machine sandbox
CN106126317A (en) * 2016-06-24 2016-11-16 安徽师范大学 Virtual machine scheduling method applied to cloud computing environment
US10277631B1 (en) * 2016-07-08 2019-04-30 Sprint Communications Company L.P. Self-preserving policy engine and policy-based content transmission
US10162966B1 (en) 2016-10-19 2018-12-25 Trend Micro Incorporated Anti-malware system with evasion code detection and rectification
US10212184B2 (en) 2016-10-27 2019-02-19 Opaq Networks, Inc. Method for the continuous calculation of a cyber security risk index
US10333975B2 (en) * 2016-12-06 2019-06-25 Vmware, Inc. Enhanced computing system security using a secure browser
US10171425B2 (en) 2016-12-15 2019-01-01 Keysight Technologies Singapore (Holdings) Pte Ltd Active firewall control for network traffic sessions within virtual processing platforms
US10178003B2 (en) 2016-12-15 2019-01-08 Keysight Technologies Singapore (Holdings) Pte Ltd Instance based management and control for VM platforms in virtual processing environments
US10142263B2 (en) 2017-02-21 2018-11-27 Keysight Technologies Singapore (Holdings) Pte Ltd Packet deduplication for network packet monitoring in virtual processing environments
US20190012456A1 (en) * 2017-07-10 2019-01-10 Centripetal Networks, Inc. Cyberanalysis Workflow Acceleration
JP6418290B2 (en) * 2017-07-19 2018-11-07 富士ゼロックス株式会社 The information processing apparatus, the image file data structures, and program
US10284526B2 (en) 2017-07-24 2019-05-07 Centripetal Networks, Inc. Efficient SSL/TLS proxy
RU2680736C1 (en) * 2018-01-17 2019-02-26 Общество с ограниченной ответственностью "Группа АйБи ТДС" Malware files in network traffic detection server and method

Citations (15)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20020032871A1 (en) * 2000-09-08 2002-03-14 The Regents Of The University Of Michigan Method and system for detecting, tracking and blocking denial of service attacks over a computer network
US20070025093A1 (en) * 2005-07-29 2007-02-01 Hon Hai Precision Industry Co., Ltd. Computer enclosure
US20070280114A1 (en) * 2006-06-06 2007-12-06 Hung-Hsiang Jonathan Chao Providing a high-speed defense against distributed denial of service (DDoS) attacks
US20080014137A1 (en) * 2004-07-02 2008-01-17 Barnett Daniel J Pseudoisothermal ammonia process
US7496961B2 (en) * 2003-10-15 2009-02-24 Intel Corporation Methods and apparatus to provide network traffic support and physical security support
US20090222558A1 (en) * 2003-09-19 2009-09-03 Vmware, Inc. Managing Network Data Transfers in a Virtual Computer System
US20090268611A1 (en) * 2008-04-28 2009-10-29 Sun Microsystems, Inc. Method and system for bandwidth control on a network interface card
US20090276774A1 (en) * 2008-05-01 2009-11-05 Junji Kinoshita Access control for virtual machines in an information system
US20090282485A1 (en) * 2008-05-12 2009-11-12 Bennett James D Network browser based virus detection
US20090300149A1 (en) * 2008-05-28 2009-12-03 James Michael Ferris Systems and methods for management of virtual appliances in cloud-based network
US7877803B2 (en) * 2005-06-27 2011-01-25 Hewlett-Packard Development Company, L.P. Automated immune response for a computer
US7908653B2 (en) * 2004-06-29 2011-03-15 Intel Corporation Method of improving computer security through sandboxing
US7941855B2 (en) * 2003-04-14 2011-05-10 New Mexico Technical Research Foundation Computationally intelligent agents for distributed intrusion detection system and method of practicing same
US8006303B1 (en) * 2007-06-07 2011-08-23 International Business Machines Corporation System, method and program product for intrusion protection of a network
US8151348B1 (en) * 2004-06-30 2012-04-03 Cisco Technology, Inc. Automatic detection of reverse tunnels

Family Cites Families (330)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
DE2851871C2 (en) 1978-11-30 1984-06-07 Siemens Ag, 1000 Berlin Und 8000 Muenchen, De
GB9003890D0 (en) 1990-02-21 1990-04-18 Rodime Plc Method and apparatus for controlling access to and corruption of information in computer systems
US5175732A (en) 1991-02-15 1992-12-29 Standard Microsystems Corp. Method and apparatus for controlling data communication operations within stations of a local-area network
US5390325A (en) * 1992-12-23 1995-02-14 Taligent, Inc. Automated testing system
US5440723A (en) 1993-01-19 1995-08-08 International Business Machines Corporation Automatic immune system for computers and computer networks
WO1995033237A1 (en) 1994-06-01 1995-12-07 Quantum Leap Innovations Inc. Computer virus trap
JPH08171482A (en) 1994-10-29 1996-07-02 Mitsubishi Electric Corp System for generating version of program
US5537540A (en) 1994-09-30 1996-07-16 Compaq Computer Corporation Transparent, secure computer virus detection method and apparatus
US7711714B2 (en) 1998-09-22 2010-05-04 Hitachi, Ltd. Method and a device for sterilizing downloaded files
US6424627B1 (en) 1997-02-24 2002-07-23 Metrobility Optical Systems Full-duplex medium tap apparatus and system
US6094677A (en) 1997-05-30 2000-07-25 International Business Machines Corporation Methods, systems and computer program products for providing insertions during delays in interactive systems
US5978917A (en) 1997-08-14 1999-11-02 Symantec Corporation Detection and elimination of macro viruses
US6357008B1 (en) 1997-09-23 2002-03-12 Symantec Corporation Dynamic heuristic method for detecting computer viruses using decryption exploration and evaluation phases
IL121898D0 (en) * 1997-10-07 1998-03-10 Cidon Israel A method and apparatus for active testing and fault allocation of communication networks
US6108799A (en) 1997-11-21 2000-08-22 International Business Machines Corporation Automated sample creation of polymorphic and non-polymorphic marcro viruses
US6088803A (en) 1997-12-30 2000-07-11 Intel Corporation System for virus-checking network data during download to a client device
US6279113B1 (en) 1998-03-16 2001-08-21 Internet Tools, Inc. Dynamic signature inspection-based network intrusion detection
US6298445B1 (en) 1998-04-30 2001-10-02 Netect, Ltd. Computer security
US6550012B1 (en) * 1998-12-11 2003-04-15 Network Associates, Inc. Active firewall system and methodology
US6487666B1 (en) 1999-01-15 2002-11-26 Cisco Technology, Inc. Intrusion detection signature analysis using regular expressions and logical operators
US6484315B1 (en) 1999-02-01 2002-11-19 Cisco Technology, Inc. Method and system for dynamically distributing updates in a network
US20030191957A1 (en) 1999-02-19 2003-10-09 Ari Hypponen Distributed computer virus detection and scanning
US7240368B1 (en) 1999-04-14 2007-07-03 Verizon Corporate Services Group Inc. Intrusion and misuse deterrence system employing a virtual network
US6430691B1 (en) 1999-06-21 2002-08-06 Copytele, Inc. Stand-alone telecommunications security device
US6442696B1 (en) 1999-10-05 2002-08-27 Authoriszor, Inc. System and method for extensible positive client identification
US6493756B1 (en) 1999-10-28 2002-12-10 Networks Associates, Inc. System and method for dynamically sensing an asynchronous network event within a modular framework for network event processing
US6775657B1 (en) 1999-12-22 2004-08-10 Cisco Technology, Inc. Multilayered intrusion detection system and method
GB2353372B (en) 1999-12-24 2001-08-22 F Secure Oyj Remote computer virus scanning
US6832367B1 (en) 2000-03-06 2004-12-14 International Business Machines Corporation Method and system for recording and replaying the execution of distributed java programs
US20010047326A1 (en) 2000-03-14 2001-11-29 Broadbent David F. Interface system for a mortgage loan originator compliance engine
US7054943B1 (en) 2000-04-28 2006-05-30 International Business Machines Corporation Method and apparatus for dynamically adjusting resources assigned to plurality of customers, for meeting service level agreements (slas) with minimal resources, and allowing common pools of resources to be used across plural customers on a demand basis
CA2414251A1 (en) 2000-05-19 2002-03-14 Self Repairing Computers, Inc. A computer with switchable components
US7240364B1 (en) 2000-05-20 2007-07-03 Ciena Corporation Network device identity authentication
US6907396B1 (en) * 2000-06-01 2005-06-14 Networks Associates Technology, Inc. Detecting computer viruses or malicious software by patching instructions into an emulator
US6971097B1 (en) 2000-06-09 2005-11-29 Sun Microsystems, Inc. Method and apparatus for implementing concurrently running jobs on an extended virtual machine using different heaps managers
US7080407B1 (en) 2000-06-27 2006-07-18 Cisco Technology, Inc. Virus detection and removal system and method for network-based systems
US7093239B1 (en) 2000-07-14 2006-08-15 Internet Security Systems, Inc. Computer immune system and method for detecting unwanted code in a computer system
JP2002035109A (en) * 2000-07-21 2002-02-05 Tadashi Kokubo Anti-thrombotic material and method for manufacturing the same
US6981279B1 (en) 2000-08-17 2005-12-27 International Business Machines Corporation Method and apparatus for replicating and analyzing worm programs
GB0022485D0 (en) 2000-09-13 2000-11-01 Apl Financial Services Oversea Monitoring network activity
US20020038430A1 (en) 2000-09-13 2002-03-28 Charles Edwards System and method of data collection, processing, analysis, and annotation for monitoring cyber-threats and the notification thereof to subscribers
US8438241B2 (en) * 2001-08-14 2013-05-07 Cisco Technology, Inc. Detecting and protecting against worm traffic on a network
US7657419B2 (en) * 2001-06-19 2010-02-02 International Business Machines Corporation Analytical virtual machine
US7496960B1 (en) 2000-10-30 2009-02-24 Trend Micro, Inc. Tracking and reporting of computer virus information
US7512980B2 (en) 2001-11-30 2009-03-31 Lancope, Inc. Packet sampling flow-based detection of network intrusions
US20020091819A1 (en) * 2001-01-05 2002-07-11 Daniel Melchione System and method for configuring computer applications and devices using inheritance
US20060047665A1 (en) 2001-01-09 2006-03-02 Tim Neil System and method for simulating an application for subsequent deployment to a device in communication with a transaction server
US20020095607A1 (en) 2001-01-18 2002-07-18 Catherine Lin-Hendel Security protection for computers and computer-networks
US7290283B2 (en) 2001-01-31 2007-10-30 Lancope, Inc. Network port profiling
GB0103416D0 (en) * 2001-02-12 2001-03-28 Nokia Networks Oy Message authentication
WO2002071227A1 (en) 2001-03-01 2002-09-12 Cyber Operations, Llc System and method for anti-network terrorism
US7770223B2 (en) 2001-04-12 2010-08-03 Computer Associates Think, Inc. Method and apparatus for security management via vicarious network devices
CN1147795C (en) 2001-04-29 2004-04-28 北京瑞星科技股份有限公司 Method and system for detecting and clearing known and unknown computer virus
CA2446584A1 (en) * 2001-05-09 2002-11-14 Ecd Systems, Inc. Systems and methods for the prevention of unauthorized use and manipulation of digital content
US7043757B2 (en) * 2001-05-22 2006-05-09 Mci, Llc System and method for malicious code detection
US20020194490A1 (en) 2001-06-18 2002-12-19 Avner Halperin System and method of virus containment in computer networks
US7028179B2 (en) * 2001-07-03 2006-04-11 Intel Corporation Apparatus and method for secure, automated response to distributed denial of service attacks
US7096368B2 (en) 2001-08-01 2006-08-22 Mcafee, Inc. Platform abstraction layer for a wireless malware scanning engine
US7356736B2 (en) 2001-09-25 2008-04-08 Norman Asa Simulated computer system for monitoring of software performance
US7107617B2 (en) 2001-10-15 2006-09-12 Mcafee, Inc. Malware scanning of compressed computer files
US20030074578A1 (en) * 2001-10-16 2003-04-17 Richard Ford Computer virus containment
US7007107B1 (en) * 2001-10-22 2006-02-28 United Electronic Industries Methods and apparatus for performing data acquisition and control
US20030084318A1 (en) 2001-10-31 2003-05-01 Schertz Richard L. System and method of graphically correlating data for an intrusion protection system
US7320142B1 (en) 2001-11-09 2008-01-15 Cisco Technology, Inc. Method and system for configurable network intrusion detection
US20030101381A1 (en) 2001-11-29 2003-05-29 Nikolay Mateev System and method for virus checking software
US7080408B1 (en) 2001-11-30 2006-07-18 Mcafee, Inc. Delayed-delivery quarantining of network communications having suspicious contents
US7062553B2 (en) * 2001-12-04 2006-06-13 Trend Micro, Inc. Virus epidemic damage control system and method for network environment
US6895550B2 (en) * 2001-12-05 2005-05-17 I2 Technologies Us, Inc. Computer-implemented PDF document management
US7093002B2 (en) 2001-12-06 2006-08-15 Mcafee, Inc. Handling of malware scanning of files stored within a file storage device of a computer network
NZ516346A (en) 2001-12-21 2004-09-24 Esphion Ltd A device for evaluating traffic on a computer network to detect traffic abnormalities such as a denial of service attack
US7607171B1 (en) 2002-01-17 2009-10-20 Avinti, Inc. Virus detection by executing e-mail code in a virtual machine
US7100201B2 (en) 2002-01-24 2006-08-29 Arxceo Corporation Undetectable firewall
US7448084B1 (en) * 2002-01-25 2008-11-04 The Trustees Of Columbia University In The City Of New York System and methods for detecting intrusions in a computer system by monitoring operating system registry accesses
US7069316B1 (en) 2002-02-19 2006-06-27 Mcafee, Inc. Automated Internet Relay Chat malware monitoring and interception
JP3713491B2 (en) 2002-02-28 2005-11-09 株式会社エヌ・ティ・ティ・ドコモ Server device, and information processing method
US7458098B2 (en) 2002-03-08 2008-11-25 Secure Computing Corporation Systems and methods for enhancing electronic communication security
US20030188190A1 (en) 2002-03-26 2003-10-02 Aaron Jeffrey A. System and method of intrusion detection employing broad-scope monitoring
US7370360B2 (en) 2002-05-13 2008-05-06 International Business Machines Corporation Computer immune system and method for detecting unwanted code in a P-code or partially compiled native-code program executing within a virtual machine
US7415723B2 (en) 2002-06-11 2008-08-19 Pandya Ashish A Distributed network security system and a hardware processor therefor
US8539580B2 (en) 2002-06-19 2013-09-17 International Business Machines Corporation Method, system and program product for detecting intrusion of a wireless network
US8423374B2 (en) 2002-06-27 2013-04-16 Siebel Systems, Inc. Method and system for processing intelligence information
US7124327B2 (en) 2002-06-29 2006-10-17 Intel Corporation Control over faults occurring during the operation of guest software in the virtual-machine architecture
US7418729B2 (en) 2002-07-19 2008-08-26 Symantec Corporation Heuristic detection of malicious computer code by page tracking
US8788650B1 (en) * 2002-07-19 2014-07-22 Fortinet, Inc. Hardware based detection devices for detecting network traffic content and methods of using the same
US7487543B2 (en) 2002-07-23 2009-02-03 International Business Machines Corporation Method and apparatus for the automatic determination of potentially worm-like behavior of a program
JP3794491B2 (en) 2002-08-20 2006-07-05 日本電気株式会社 Attack defense system and Allegations
US7251215B1 (en) 2002-08-26 2007-07-31 Juniper Networks, Inc. Adaptive network router
US20040047356A1 (en) 2002-09-06 2004-03-11 Bauer Blaine D. Network traffic monitoring
US7467408B1 (en) 2002-09-09 2008-12-16 Cisco Technology, Inc. Method and apparatus for capturing and filtering datagrams for network security monitoring
GB0220907D0 (en) 2002-09-10 2002-10-16 Ingenia Holdings Ltd Security device and system
US7159149B2 (en) 2002-10-24 2007-01-02 Symantec Corporation Heuristic detection and termination of fast spreading network worm attacks
US7363656B2 (en) 2002-11-04 2008-04-22 Mazu Networks, Inc. Event detection/anomaly correlation heuristics
US20050033989A1 (en) 2002-11-04 2005-02-10 Poletto Massimiliano Antonio Detection of scanning attacks
US7454499B2 (en) * 2002-11-07 2008-11-18 Tippingpoint Technologies, Inc. Active network defense system and method
US20040111531A1 (en) * 2002-12-06 2004-06-10 Stuart Staniford Method and system for reducing the rate of infection of a communications network by a software worm
US7428300B1 (en) 2002-12-09 2008-09-23 Verizon Laboratories Inc. Diagnosing fault patterns in telecommunication networks
US20040128355A1 (en) 2002-12-25 2004-07-01 Kuo-Jen Chao Community-based message classification and self-amending system for a messaging system
US7949785B2 (en) 2003-03-31 2011-05-24 Inpro Network Facility, Llc Secure virtual community network system
US6898632B2 (en) 2003-03-31 2005-05-24 Finisar Corporation Network security tap for use with intrusion detection system
US7607010B2 (en) 2003-04-12 2009-10-20 Deep Nines, Inc. System and method for network edge data protection
US8640234B2 (en) 2003-05-07 2014-01-28 Trustwave Holdings, Inc. Method and apparatus for predictive and actual intrusion detection on a network
US7308716B2 (en) * 2003-05-20 2007-12-11 International Business Machines Corporation Applying blocking measures progressively to malicious network traffic
US7464404B2 (en) * 2003-05-20 2008-12-09 International Business Machines Corporation Method of responding to a truncated secure session attack
US7231667B2 (en) 2003-05-29 2007-06-12 Computer Associates Think, Inc. System and method for computer virus detection utilizing heuristic analysis
US7543051B2 (en) * 2003-05-30 2009-06-02 Borland Software Corporation Method of non-intrusive analysis of secure and non-secure web application traffic in real-time
US20050108562A1 (en) * 2003-06-18 2005-05-19 Khazan Roger I. Technique for detecting executable malicious code using a combination of static and dynamic analyses
JP4734240B2 (en) 2003-06-18 2011-07-27 インテリシンク コーポレイション System and method for providing a notification to the remote device
US8627457B2 (en) 2003-06-30 2014-01-07 Verizon Business Global Llc Integrated security system
US7392542B2 (en) * 2003-08-29 2008-06-24 Seagate Technology Llc Restoration of data corrupted by viruses using pre-infected copy of data
US7565550B2 (en) 2003-08-29 2009-07-21 Trend Micro, Inc. Automatic registration of a virus/worm monitor in a distributed network
KR100432675B1 (en) * 2003-09-19 2004-05-12 주식회사 아이앤아이맥스 Method of controlling communication between equipments on a network and apparatus for the same
US7644441B2 (en) * 2003-09-26 2010-01-05 Cigital, Inc. Methods for identifying malicious software
US7987293B2 (en) * 2004-10-04 2011-07-26 Netmask (El-Mar) Internet Technologies Ltd. Dynamic content conversion
WO2005043360A1 (en) 2003-10-21 2005-05-12 Green Border Technologies Systems and methods for secure client applications
US7584455B2 (en) 2003-10-23 2009-09-01 Microsoft Corporation Predicate-based test coverage and generation
JP4051020B2 (en) * 2003-10-28 2008-02-20 富士通株式会社 Worm determination program, a computer readable storing the worm determination program storage medium, a worm determination method and worms determination apparatus
JP3999188B2 (en) 2003-10-28 2007-10-31 富士通株式会社 Unauthorized access detection device, unauthorized access detection method and unauthorized access detection program
US7421689B2 (en) 2003-10-28 2008-09-02 Hewlett-Packard Development Company, L.P. Processor-architecture for facilitating a virtual machine monitor
ES2423491T3 (en) 2003-11-12 2013-09-20 The Trustees Of Columbia University In The City Of New York Apparatus, method and means for detecting an abnormality payload distribution using n-gram normal data
US20050114663A1 (en) 2003-11-21 2005-05-26 Finisar Corporation Secure network access devices with data encryption
US20050201297A1 (en) 2003-12-12 2005-09-15 Cyrus Peikari Diagnosis of embedded, wireless mesh networks with real-time, flexible, location-specific signaling
WO2005071923A1 (en) 2004-01-20 2005-08-04 Intrusic, Inc Systems and methods for monitoring data transmissions to detect a compromised network
JP2007528059A (en) 2004-01-22 2007-10-04 エヌイーシー ラボラトリーズ アメリカ インクNEC Laboratories America, Inc. Modeling software, abstract, and systems and methods for analysis
US8220055B1 (en) * 2004-02-06 2012-07-10 Symantec Corporation Behavior blocking utilizing positive behavior system and method
US7530104B1 (en) * 2004-02-09 2009-05-05 Symantec Corporation Threat analysis
US20050183143A1 (en) 2004-02-13 2005-08-18 Anderholm Eric J. Methods and systems for monitoring user, application or device activity
US8584239B2 (en) * 2004-04-01 2013-11-12 Fireeye, Inc. Virtual machine with dynamic data flow analysis
US8561177B1 (en) 2004-04-01 2013-10-15 Fireeye, Inc. Systems and methods for detecting communication channels of bots
US9106694B2 (en) 2004-04-01 2015-08-11 Fireeye, Inc. Electronic message analysis for malware detection
US8566946B1 (en) 2006-04-20 2013-10-22 Fireeye, Inc. Malware containment on connection
US8375444B2 (en) * 2006-04-20 2013-02-12 Fireeye, Inc. Dynamic signature creation and enforcement
US8204984B1 (en) 2004-04-01 2012-06-19 Fireeye, Inc. Systems and methods for detecting encrypted bot command and control communication channels
US8171553B2 (en) 2004-04-01 2012-05-01 Fireeye, Inc. Heuristic based capture with replay to virtual machine
US8528086B1 (en) 2004-04-01 2013-09-03 Fireeye, Inc. System and method of detecting computer worms
US8793787B2 (en) 2004-04-01 2014-07-29 Fireeye, Inc. Detecting malicious network content using virtual environment components
US8539582B1 (en) 2004-04-01 2013-09-17 Fireeye, Inc. Malware containment and security analysis on connection
US7966658B2 (en) 2004-04-08 2011-06-21 The Regents Of The University Of California Detecting public network attacks using signatures and fast content analysis
US7533415B2 (en) 2004-04-21 2009-05-12 Trend Micro Incorporated Method and apparatus for controlling traffic in a computer network
US20050240781A1 (en) * 2004-04-22 2005-10-27 Gassoway Paul A Prioritizing intrusion detection logs
US7779463B2 (en) 2004-05-11 2010-08-17 The Trustees Of Columbia University In The City Of New York Systems and methods for correlating and distributing intrusion alert information among collaborating computer systems
US7441272B2 (en) 2004-06-09 2008-10-21 Intel Corporation Techniques for self-isolation of networked devices
US8006305B2 (en) * 2004-06-14 2011-08-23 Fireeye, Inc. Computer worm defense system and method
US8549638B2 (en) * 2004-06-14 2013-10-01 Fireeye, Inc. System and method of containing computer worms
US20060010495A1 (en) 2004-07-06 2006-01-12 Oded Cohen Method for protecting a computer from suspicious objects
CN100465982C (en) 2004-07-16 2009-03-04 松下电器产业株式会社 Application execution device and application execution method of the application execution device
US20060015715A1 (en) * 2004-07-16 2006-01-19 Eric Anderson Automatically protecting network service from network attack
US7444521B2 (en) 2004-07-16 2008-10-28 Red Hat, Inc. System and method for detecting computer virus
US7603715B2 (en) * 2004-07-21 2009-10-13 Microsoft Corporation Containment of worms
US20060031476A1 (en) 2004-08-05 2006-02-09 Mathes Marvin L Apparatus and method for remotely monitoring a computer network
US7949849B2 (en) 2004-08-24 2011-05-24 Mcafee, Inc. File system for a capture system
US8214901B2 (en) * 2004-09-17 2012-07-03 Sri International Method and apparatus for combating malicious code
US7434261B2 (en) 2004-09-27 2008-10-07 Microsoft Corporation System and method of identifying the source of an attack on a computer network
US7478428B1 (en) * 2004-10-12 2009-01-13 Microsoft Corporation Adapting input to find integer overflows
US20060101516A1 (en) 2004-10-12 2006-05-11 Sushanthan Sudaharan Honeynet farms as an early warning system for production networks
US7849506B1 (en) 2004-10-12 2010-12-07 Avaya Inc. Switching device, method, and computer program for efficient intrusion detection
US7610375B2 (en) 2004-10-28 2009-10-27 Cisco Technology, Inc. Intrusion detection in a data center environment
US20060101517A1 (en) * 2004-10-28 2006-05-11 Banzhof Carl E Inventory management-based computer vulnerability resolution system
WO2007001439A2 (en) * 2004-11-04 2007-01-04 Telcordia Technologies, Inc. Detecting exploit code in network flows
US20060101277A1 (en) 2004-11-10 2006-05-11 Meenan Patrick A Detecting and remedying unauthorized computer programs
US7540025B2 (en) * 2004-11-18 2009-05-26 Cisco Technology, Inc. Mitigating network attacks using automatic signature generation
US7784097B1 (en) 2004-11-24 2010-08-24 The Trustees Of Columbia University In The City Of New York Systems and methods for correlating and distributing intrusion alert information among collaborating computer systems
US20060117385A1 (en) * 2004-11-30 2006-06-01 Mester Michael L Monitoring propagation protection within a network
US7987272B2 (en) 2004-12-06 2011-07-26 Cisco Technology, Inc. Performing message payload processing functions in a network element on behalf of an application
US20060161989A1 (en) 2004-12-13 2006-07-20 Eran Reshef System and method for deterring rogue users from attacking protected legitimate users
US7937761B1 (en) * 2004-12-17 2011-05-03 Symantec Corporation Differential threat detection processing
US20060143709A1 (en) * 2004-12-27 2006-06-29 Raytheon Company Network intrusion prevention
US7725938B2 (en) 2005-01-20 2010-05-25 Cisco Technology, Inc. Inline intrusion detection
US20060164199A1 (en) 2005-01-26 2006-07-27 Lockdown Networks, Inc. Network appliance for securely quarantining a node on a network
US7676841B2 (en) 2005-02-01 2010-03-09 Fmr Llc Network intrusion mitigation
US7668962B2 (en) 2005-02-07 2010-02-23 Symantec Operating Corporation System and method for connection failover using redirection
US7904518B2 (en) 2005-02-15 2011-03-08 Gytheion Networks Llc Apparatus and method for analyzing and filtering email and for providing web related services
US7784099B2 (en) 2005-02-18 2010-08-24 Pace University System for intrusion detection and vulnerability assessment in a computer network using simulation and machine learning
US7516488B1 (en) 2005-02-23 2009-04-07 Symantec Corporation Preventing data from being submitted to a remote system in response to a malicious e-mail
JP2006270193A (en) 2005-03-22 2006-10-05 Fuji Xerox Co Ltd Image forming system and method, and image forming apparatus
US7650639B2 (en) 2005-03-31 2010-01-19 Microsoft Corporation System and method for protecting a limited resource computer from malware
US20060221956A1 (en) 2005-03-31 2006-10-05 Narayan Harsha L Methods for performing packet classification via prefix pair bit vectors
JP4630706B2 (en) 2005-03-31 2011-02-09 富士通株式会社 Service device, the client device by the service device connection destination changeover control method, and program
US7568233B1 (en) 2005-04-01 2009-07-28 Symantec Corporation Detecting malicious software through process dump scanning
WO2006107712A2 (en) 2005-04-04 2006-10-12 Bae Systems Information And Electronic Systems Integration Inc. Method and apparatus for defending against zero-day worm-based attacks
AU2006236283A1 (en) 2005-04-18 2006-10-26 The Trustees Of Columbia University In The City Of New York Systems and methods for detecting and inhibiting attacks using honeypots
US7603712B2 (en) 2005-04-21 2009-10-13 Microsoft Corporation Protecting a computer that provides a Web service from malware
US8069250B2 (en) 2005-04-28 2011-11-29 Vmware, Inc. One-way proxy system
US7493602B2 (en) 2005-05-02 2009-02-17 International Business Machines Corporation Methods and arrangements for unified program analysis
US7480773B1 (en) * 2005-05-02 2009-01-20 Sprint Communications Company L.P. Virtual machine use and optimization of hardware configurations
EP1877904B1 (en) 2005-05-05 2015-12-30 Cisco IronPort Systems LLC Detecting unwanted electronic mail messages based on probabilistic analysis of referenced resources
US7930738B1 (en) * 2005-06-02 2011-04-19 Adobe Systems Incorporated Method and apparatus for secure execution of code
US8272054B2 (en) 2005-06-06 2012-09-18 International Business Machines Corporation Computer network intrusion detection system and method
US20060288417A1 (en) 2005-06-21 2006-12-21 Sbc Knowledge Ventures Lp Method and apparatus for mitigating the effects of malicious software in a communication network
US7636938B2 (en) * 2005-06-30 2009-12-22 Microsoft Corporation Controlling network access
US20070016951A1 (en) * 2005-07-13 2007-01-18 Piccard Paul L Systems and methods for identifying sources of malware
US7984493B2 (en) * 2005-07-22 2011-07-19 Alcatel-Lucent DNS based enforcement for confinement and detection of network malicious activities
US7797387B2 (en) * 2005-08-15 2010-09-14 Cisco Technology, Inc. Interactive text communication system
US8407785B2 (en) 2005-08-18 2013-03-26 The Trustees Of Columbia University In The City Of New York Systems, methods, and media protecting a digital data processing device from attack
US7739740B1 (en) * 2005-09-22 2010-06-15 Symantec Corporation Detecting polymorphic threats
US7725737B2 (en) * 2005-10-14 2010-05-25 Check Point Software Technologies, Inc. System and methodology providing secure workspace environment
US7730011B1 (en) 2005-10-19 2010-06-01 Mcafee, Inc. Attributes of captured objects in a capture system
CN100428157C (en) 2005-10-19 2008-10-22 联想(北京)有限公司 A computer system and method to check completely
US7971256B2 (en) * 2005-10-20 2011-06-28 Cisco Technology, Inc. Mechanism to correlate the presence of worms in a network
US9055093B2 (en) * 2005-10-21 2015-06-09 Kevin R. Borders Method, system and computer program product for detecting at least one of security threats and undesirable computer files
WO2007050244A2 (en) 2005-10-27 2007-05-03 Georgia Tech Research Corporation Method and system for detecting and responding to attacking networks
US7698548B2 (en) 2005-12-08 2010-04-13 Microsoft Corporation Communications traffic segregation for security purposes
US20070143827A1 (en) 2005-12-21 2007-06-21 Fiberlink Methods and systems for intelligently controlling access to computing resources
US7849143B2 (en) 2005-12-29 2010-12-07 Research In Motion Limited System and method of dynamic management of spam
US8255996B2 (en) 2005-12-30 2012-08-28 Extreme Networks, Inc. Network threat detection and mitigation
WO2007076624A1 (en) * 2005-12-30 2007-07-12 Intel Corporation Virtual machine to detect malicious code
US8533680B2 (en) 2005-12-30 2013-09-10 Microsoft Corporation Approximating finite domains in symbolic state exploration
US8209667B2 (en) 2006-01-11 2012-06-26 International Business Machines Corporation Software verification using hybrid explicit and symbolic model checking
US8196205B2 (en) 2006-01-23 2012-06-05 University Of Washington Through Its Center For Commercialization Detection of spyware threats within virtual machine
US8018845B2 (en) 2006-01-25 2011-09-13 Cisco Technology, Inc Sampling rate-limited traffic
US20070192500A1 (en) 2006-02-16 2007-08-16 Infoexpress, Inc. Network access control including dynamic policy enforcement point
US20070192858A1 (en) 2006-02-16 2007-08-16 Infoexpress, Inc. Peer based network access control
US8176480B1 (en) 2006-02-27 2012-05-08 Symantec Operating Corporation Adaptive instrumentation through dynamic recompilation
WO2007100916A2 (en) 2006-02-28 2007-09-07 The Trustees Of Columbia University In The City Of New York Systems, methods, and media for outputting a dataset based upon anomaly detection
EP1999925B1 (en) * 2006-03-27 2011-07-06 Telecom Italia S.p.A. A method and system for identifying malicious messages in mobile communication networks, related network and computer program product therefor
US7757112B2 (en) 2006-03-29 2010-07-13 Lenovo (Singapore) Pte. Ltd. System and method for booting alternate MBR in event of virus attack
US8479174B2 (en) 2006-04-05 2013-07-02 Prevx Limited Method, computer program and computer for analyzing an executable computer file
WO2007117585A2 (en) * 2006-04-06 2007-10-18 Smobile Systems Inc. System and method for managing malware protection on mobile devices
US8510827B1 (en) 2006-05-18 2013-08-13 Vmware, Inc. Taint tracking mechanism for computer security
US8365286B2 (en) * 2006-06-30 2013-01-29 Sophos Plc Method and system for classification of software using characteristics and combinations of such characteristics
US8261344B2 (en) * 2006-06-30 2012-09-04 Sophos Plc Method and system for classification of software using characteristics and combinations of such characteristics
US8020206B2 (en) * 2006-07-10 2011-09-13 Websense, Inc. System and method of analyzing web content
US20080077793A1 (en) * 2006-09-21 2008-03-27 Sensory Networks, Inc. Apparatus and method for high throughput network security systems
US8533819B2 (en) * 2006-09-29 2013-09-10 At&T Intellectual Property Ii, L.P. Method and apparatus for detecting compromised host computers
WO2008041950A2 (en) 2006-10-04 2008-04-10 Trek 2000 International Ltd. Method, apparatus and system for authentication of external storage devices
DE102006047979B4 (en) 2006-10-10 2009-07-16 OCé PRINTING SYSTEMS GMBH A data processing system, method and computer program product for performing a test routine in conjunction with an operating system
US7832008B1 (en) 2006-10-11 2010-11-09 Cisco Technology, Inc. Protection of computer resources
US8234640B1 (en) 2006-10-17 2012-07-31 Manageiq, Inc. Compliance-based adaptations in managed virtual systems
US8949826B2 (en) * 2006-10-17 2015-02-03 Managelq, Inc. Control and management of virtual systems
US8042184B1 (en) 2006-10-18 2011-10-18 Kaspersky Lab, Zao Rapid analysis of data stream for malware presence
US20080141376A1 (en) * 2006-10-24 2008-06-12 Pc Tools Technology Pty Ltd. Determining maliciousness of software
US8656495B2 (en) * 2006-11-17 2014-02-18 Hewlett-Packard Development Company, L.P. Web application assessment based on intelligent generation of attack strings
KR100922579B1 (en) 2006-11-30 2009-10-21 한국전자통신연구원 Apparatus and method for detecting network attack
GB2444514A (en) 2006-12-04 2008-06-11 Glasswall Electronic file re-generation
US8904535B2 (en) * 2006-12-20 2014-12-02 The Penn State Research Foundation Proactive worm containment (PWC) for enterprise networks
AT438151T (en) 2006-12-21 2009-08-15 Ericsson Telefon Ab L M Concealment of computer program codes
CN101573653B (en) 2006-12-26 2011-03-16 夏普株式会社 Backlight device, display, and TV receiver
US7996836B1 (en) 2006-12-29 2011-08-09 Symantec Corporation Using a hypervisor to provide computer security
GB2458094A (en) 2007-01-09 2009-09-09 Surfcontrol On Demand Ltd URL interception and categorization in firewalls
US8069484B2 (en) * 2007-01-25 2011-11-29 Mandiant Corporation System and method for determining data entropy to identify malware
US8380987B2 (en) 2007-01-25 2013-02-19 Microsoft Corporation Protection agents and privilege modes
US7908660B2 (en) * 2007-02-06 2011-03-15 Microsoft Corporation Dynamic risk management
US20080201778A1 (en) * 2007-02-21 2008-08-21 Matsushita Electric Industrial Co., Ltd. Intrusion detection using system call monitors on a bayesian network
US9021590B2 (en) * 2007-02-28 2015-04-28 Microsoft Technology Licensing, Llc Spyware detection mechanism
US20080222728A1 (en) 2007-03-05 2008-09-11 Paula Natasha Chavez Methods and interfaces for executable code analysis
US20080222729A1 (en) 2007-03-05 2008-09-11 Songqing Chen Containment of Unknown and Polymorphic Fast Spreading Worms
US8392997B2 (en) 2007-03-12 2013-03-05 University Of Southern California Value-adaptive security threat modeling and vulnerability ranking
US20080320594A1 (en) 2007-03-19 2008-12-25 Xuxian Jiang Malware Detector
US8955122B2 (en) 2007-04-04 2015-02-10 Sri International Method and apparatus for detecting malware infection
US9083712B2 (en) 2007-04-04 2015-07-14 Sri International Method and apparatus for generating highly predictive blacklists
US7904961B2 (en) 2007-04-20 2011-03-08 Juniper Networks, Inc. Network attack detection using partial deterministic finite automaton pattern matching
US20080295172A1 (en) * 2007-05-22 2008-11-27 Khushboo Bohacek Method, system and computer-readable media for reducing undesired intrusion alarms in electronic communications systems and networks
US8402529B1 (en) * 2007-05-30 2013-03-19 M86 Security, Inc. Preventing propagation of malicious software during execution in a virtual machine
GB2449852A (en) * 2007-06-04 2008-12-10 Agilent Technologies Inc Monitoring network attacks using pattern matching
US7853689B2 (en) 2007-06-15 2010-12-14 Broadcom Corporation Multi-stage deep packet inspection for lightweight devices
US20090007100A1 (en) 2007-06-28 2009-01-01 Microsoft Corporation Suspending a Running Operating System to Enable Security Scanning
US8584094B2 (en) * 2007-06-29 2013-11-12 Microsoft Corporation Dynamically computing reputation scores for objects
US7836502B1 (en) * 2007-07-03 2010-11-16 Trend Micro Inc. Scheduled gateway scanning arrangement and methods thereof
US20090013408A1 (en) * 2007-07-06 2009-01-08 Messagelabs Limited Detection of exploits in files
US8448161B2 (en) 2007-07-30 2013-05-21 Adobe Systems Incorporated Application tracking for application execution environment
US8060074B2 (en) 2007-07-30 2011-11-15 Mobile Iron, Inc. Virtual instance architecture for mobile device management systems
US8621610B2 (en) 2007-08-06 2013-12-31 The Regents Of The University Of Michigan Network service for the detection, analysis and quarantine of malicious and unwanted files
US8763115B2 (en) * 2007-08-08 2014-06-24 Vmware, Inc. Impeding progress of malicious guest software
US8601451B2 (en) 2007-08-29 2013-12-03 Mcafee, Inc. System, method, and computer program product for determining whether code is unwanted based on the decompilation thereof
KR101377014B1 (en) * 2007-09-04 2014-03-26 삼성전자주식회사 System and Method of Malware Diagnosis Mechanism Based on Immune Database
US8307443B2 (en) * 2007-09-28 2012-11-06 Microsoft Corporation Securing anti-virus software with virtualization
US7559086B2 (en) 2007-10-02 2009-07-07 Kaspersky Lab, Zao System and method for detecting multi-component malware
US8019700B2 (en) * 2007-10-05 2011-09-13 Google Inc. Detecting an intrusive landing page
US8819676B2 (en) 2007-10-30 2014-08-26 Vmware, Inc. Transparent memory-mapped emulation of I/O calls
US8045458B2 (en) * 2007-11-08 2011-10-25 Mcafee, Inc. Prioritizing network traffic
US8302080B2 (en) 2007-11-08 2012-10-30 Ntt Docomo, Inc. Automated test input generation for web applications
KR100942795B1 (en) * 2007-11-21 2010-02-18 한국전자통신연구원 A method and a device for malware detection
US7797748B2 (en) 2007-12-12 2010-09-14 Vmware, Inc. On-access anti-virus mechanism for virtual machine architecture
US7996904B1 (en) 2007-12-19 2011-08-09 Symantec Corporation Automated unpacking of executables packed by multiple layers of arbitrary packers
US8510828B1 (en) 2007-12-31 2013-08-13 Symantec Corporation Enforcing the execution exception to prevent packers from evading the scanning of dynamically created code
US8225288B2 (en) 2008-01-29 2012-07-17 Intuit Inc. Model-based testing using branches, decisions, and options
US20100031353A1 (en) 2008-02-04 2010-02-04 Microsoft Corporation Malware Detection Using Code Analysis and Behavior Monitoring
US8595834B2 (en) 2008-02-04 2013-11-26 Samsung Electronics Co., Ltd Detecting unauthorized use of computing devices based on behavioral patterns
US8201246B1 (en) * 2008-02-25 2012-06-12 Trend Micro Incorporated Preventing malicious codes from performing malicious actions in a computer system
US20090228233A1 (en) * 2008-03-06 2009-09-10 Anderson Gary F Rank-based evaluation
US8407784B2 (en) 2008-03-19 2013-03-26 Websense, Inc. Method and system for protection against information stealing software
US9264441B2 (en) 2008-03-24 2016-02-16 Hewlett Packard Enterprise Development Lp System and method for securing a network from zero-day vulnerability exploits
US8239944B1 (en) * 2008-03-28 2012-08-07 Symantec Corporation Reducing malware signature set size through server-side processing
US8549486B2 (en) 2008-04-21 2013-10-01 Microsoft Corporation Active property checking
WO2010011411A1 (en) * 2008-05-27 2010-01-28 The Trustees Of Columbia University In The City Of New York Systems, methods, and media for detecting network anomalies
US8732825B2 (en) * 2008-05-28 2014-05-20 Symantec Corporation Intelligent hashes for centralized malware detection
US8516478B1 (en) * 2008-06-12 2013-08-20 Mcafee, Inc. Subsequent processing of scanning task utilizing subset of virtual machines predetermined to have scanner process and adjusting amount of subsequest VMs processing based on load
US8234709B2 (en) * 2008-06-20 2012-07-31 Symantec Operating Corporation Streaming malware definition updates
US8381298B2 (en) * 2008-06-30 2013-02-19 Microsoft Corporation Malware detention for suspected malware
US8850570B1 (en) * 2008-06-30 2014-09-30 Symantec Corporation Filter-based identification of malicious websites
US8087086B1 (en) * 2008-06-30 2011-12-27 Symantec Corporation Method for mitigating false positive generation in antivirus software
US7996475B2 (en) 2008-07-03 2011-08-09 Barracuda Networks Inc Facilitating transmission of email by checking email parameters with a database of well behaved senders
US10027688B2 (en) * 2008-08-11 2018-07-17 Damballa, Inc. Method and system for detecting malicious and/or botnet-related domain names
JP5446167B2 (en) 2008-08-13 2014-03-19 富士通株式会社 Anti-virus method, computer, and program
US20100058474A1 (en) 2008-08-29 2010-03-04 Avg Technologies Cz, S.R.O. System and method for the detection of malware
JP4521456B2 (en) * 2008-09-05 2010-08-11 株式会社東芝 Control method for an information processing system and information processing system
US8667583B2 (en) * 2008-09-22 2014-03-04 Microsoft Corporation Collecting and analyzing malware data
US8931086B2 (en) * 2008-09-26 2015-01-06 Symantec Corporation Method and apparatus for reducing false positive detection of malware
US8028338B1 (en) * 2008-09-30 2011-09-27 Symantec Corporation Modeling goodware characteristics to reduce false positive malware signatures
US8171201B1 (en) 2008-10-07 2012-05-01 Vizioncore, Inc. Systems and methods for improving virtual machine performance
US20100100718A1 (en) * 2008-10-20 2010-04-22 Novell, Inc. In-the-flow security services for guested virtual machines
US8347386B2 (en) * 2008-10-21 2013-01-01 Lookout, Inc. System and method for server-coupled malware prevention
US8850571B2 (en) 2008-11-03 2014-09-30 Fireeye, Inc. Systems and methods for detecting malicious network content
US8997219B2 (en) 2008-11-03 2015-03-31 Fireeye, Inc. Systems and methods for detecting malicious PDF network content
US8239496B2 (en) 2009-03-13 2012-08-07 Docusign, Inc. Systems and methods for document management transformation and security
US20100251104A1 (en) 2009-03-27 2010-09-30 Litera Technology Llc. System and method for reflowing content in a structured portable document format (pdf) file
US9154364B1 (en) * 2009-04-25 2015-10-06 Dasient, Inc. Monitoring for problems and detecting malware
US8090797B2 (en) 2009-05-02 2012-01-03 Citrix Systems, Inc. Methods and systems for launching applications into existing isolation environments
US8233882B2 (en) 2009-06-26 2012-07-31 Vmware, Inc. Providing security in mobile devices via a virtualization software layer
US20110041179A1 (en) * 2009-08-11 2011-02-17 F-Secure Oyj Malware detection
WO2011027352A1 (en) 2009-09-03 2011-03-10 Mcafee, Inc. Network access control
US8832829B2 (en) * 2009-09-30 2014-09-09 Fireeye, Inc. Network-based binary file extraction and analysis for malware detection
US20110111863A1 (en) * 2009-11-12 2011-05-12 Daniel Kaminsky Method and apparatus for securing networked gaming devices
US8528091B2 (en) 2009-12-31 2013-09-03 The Trustees Of Columbia University In The City Of New York Methods, systems, and media for detecting covert malware
US8307435B1 (en) 2010-02-18 2012-11-06 Symantec Corporation Software object corruption detection
US8863279B2 (en) 2010-03-08 2014-10-14 Raytheon Company System and method for malware detection
US8566944B2 (en) 2010-04-27 2013-10-22 Microsoft Corporation Malware investigation by analyzing computer memory
US8914879B2 (en) 2010-06-11 2014-12-16 Trustwave Holdings, Inc. System and method for improving coverage for web code
US8627476B1 (en) * 2010-07-05 2014-01-07 Symantec Corporation Altering application behavior based on content provider reputation
US8584234B1 (en) 2010-07-07 2013-11-12 Symantec Corporation Secure network cache content
RU2446459C1 (en) * 2010-07-23 2012-03-27 Закрытое акционерное общество "Лаборатория Касперского" System and method for checking web resources for presence of malicious components
AU2011293160B2 (en) * 2010-08-26 2015-04-09 Verisign, Inc. Method and system for automatic detection and analysis of malware
US8869277B2 (en) * 2010-09-30 2014-10-21 Microsoft Corporation Realtime multiple engine selection and combining
US9100425B2 (en) 2010-12-01 2015-08-04 Cisco Technology, Inc. Method and apparatus for detecting malicious software using generic signatures
US8479276B1 (en) * 2010-12-29 2013-07-02 Emc Corporation Malware detection using risk analysis based on file system and network activity
US9118712B2 (en) 2010-12-30 2015-08-25 Everis, Inc. Network communication system with improved security
US8510842B2 (en) 2011-04-13 2013-08-13 International Business Machines Corporation Pinpointing security vulnerabilities in computer software applications
US8640246B2 (en) 2011-06-27 2014-01-28 Raytheon Company Distributed malware detection
US20130160130A1 (en) * 2011-12-20 2013-06-20 Kirill Mendelev Application security testing
US20130160131A1 (en) * 2011-12-20 2013-06-20 Matias Madou Application security testing
US9519782B2 (en) 2012-02-24 2016-12-13 Fireeye, Inc. Detecting malicious network content

Patent Citations (15)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20020032871A1 (en) * 2000-09-08 2002-03-14 The Regents Of The University Of Michigan Method and system for detecting, tracking and blocking denial of service attacks over a computer network
US7941855B2 (en) * 2003-04-14 2011-05-10 New Mexico Technical Research Foundation Computationally intelligent agents for distributed intrusion detection system and method of practicing same
US20090222558A1 (en) * 2003-09-19 2009-09-03 Vmware, Inc. Managing Network Data Transfers in a Virtual Computer System
US7496961B2 (en) * 2003-10-15 2009-02-24 Intel Corporation Methods and apparatus to provide network traffic support and physical security support
US7908653B2 (en) * 2004-06-29 2011-03-15 Intel Corporation Method of improving computer security through sandboxing
US8151348B1 (en) * 2004-06-30 2012-04-03 Cisco Technology, Inc. Automatic detection of reverse tunnels
US20080014137A1 (en) * 2004-07-02 2008-01-17 Barnett Daniel J Pseudoisothermal ammonia process
US7877803B2 (en) * 2005-06-27 2011-01-25 Hewlett-Packard Development Company, L.P. Automated immune response for a computer
US20070025093A1 (en) * 2005-07-29 2007-02-01 Hon Hai Precision Industry Co., Ltd. Computer enclosure
US20070280114A1 (en) * 2006-06-06 2007-12-06 Hung-Hsiang Jonathan Chao Providing a high-speed defense against distributed denial of service (DDoS) attacks
US8006303B1 (en) * 2007-06-07 2011-08-23 International Business Machines Corporation System, method and program product for intrusion protection of a network
US20090268611A1 (en) * 2008-04-28 2009-10-29 Sun Microsystems, Inc. Method and system for bandwidth control on a network interface card
US20090276774A1 (en) * 2008-05-01 2009-11-05 Junji Kinoshita Access control for virtual machines in an information system
US20090282485A1 (en) * 2008-05-12 2009-11-12 Bennett James D Network browser based virus detection
US20090300149A1 (en) * 2008-05-28 2009-12-03 James Michael Ferris Systems and methods for management of virtual appliances in cloud-based network

Cited By (40)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US9838411B1 (en) 2004-04-01 2017-12-05 Fireeye, Inc. Subscriber based protection system
US10097573B1 (en) 2004-04-01 2018-10-09 Fireeye, Inc. Systems and methods for malware defense
US9661018B1 (en) 2004-04-01 2017-05-23 Fireeye, Inc. System and method for detecting anomalous behaviors using a virtual machine environment
US9792196B1 (en) 2013-02-23 2017-10-17 Fireeye, Inc. Framework for efficient security coverage of mobile software applications
US10296437B2 (en) 2013-02-23 2019-05-21 Fireeye, Inc. Framework for efficient security coverage of mobile software applications
US10025927B1 (en) 2013-03-13 2018-07-17 Fireeye, Inc. Malicious content analysis with multi-version application support within single operating environment
US9912698B1 (en) 2013-03-13 2018-03-06 Fireeye, Inc. Malicious content analysis using simulated user interaction without user involvement
US9934381B1 (en) 2013-03-13 2018-04-03 Fireeye, Inc. System and method for detecting malicious activity based on at least one environmental property
US9641546B1 (en) 2013-03-14 2017-05-02 Fireeye, Inc. Electronic device for aggregation, correlation and consolidation of analysis attributes
US10200384B1 (en) 2013-03-14 2019-02-05 Fireeye, Inc. Distributed systems and methods for automatically detecting unknown bots and botnets
US10122746B1 (en) 2013-03-14 2018-11-06 Fireeye, Inc. Correlation and consolidation of analytic data for holistic view of malware attack
US9497213B2 (en) 2013-03-15 2016-11-15 Fireeye, Inc. System and method to manage sinkholes
US9824211B2 (en) 2013-03-15 2017-11-21 Fireeye, Inc. System and method to visualize user sessions
US10083302B1 (en) 2013-06-24 2018-09-25 Fireeye, Inc. System and method for detecting time-bomb malware
US9560059B1 (en) 2013-11-21 2017-01-31 Fireeye, Inc. System, apparatus and method for conducting on-the-fly decryption of encrypted objects for malware detection
US9787700B1 (en) 2014-03-28 2017-10-10 Fireeye, Inc. System and method for offloading packet processing and static analysis operations
US9838408B1 (en) 2014-06-26 2017-12-05 Fireeye, Inc. System, device and method for detecting a malicious attack based on direct communications between remotely hosted virtual machines and malicious web servers
US9661009B1 (en) 2014-06-26 2017-05-23 Fireeye, Inc. Network-based malware detection
US9912644B2 (en) 2014-08-05 2018-03-06 Fireeye, Inc. System and method to communicate sensitive information via one or more untrusted intermediate nodes with resilience to disconnected network topology
US9609007B1 (en) 2014-08-22 2017-03-28 Fireeye, Inc. System and method of detecting delivery of malware based on indicators of compromise from different sources
US10027696B1 (en) 2014-08-22 2018-07-17 Fireeye, Inc. System and method for determining a threat based on correlation of indicators of compromise from other sources
US10075455B2 (en) 2014-12-26 2018-09-11 Fireeye, Inc. Zero-day rotating guest image profile
US9438613B1 (en) 2015-03-30 2016-09-06 Fireeye, Inc. Dynamic content activation for automated analysis of embedded objects
US9846776B1 (en) 2015-03-31 2017-12-19 Fireeye, Inc. System and method for detecting file altering behaviors pertaining to a malicious attack
US10104102B1 (en) 2015-04-13 2018-10-16 Fireeye, Inc. Analytic-based security with learning adaptability
US10216927B1 (en) 2015-06-30 2019-02-26 Fireeye, Inc. System and method for protecting memory pages associated with a process using a virtualization layer
US10176321B2 (en) 2015-09-22 2019-01-08 Fireeye, Inc. Leveraging behavior-based rules for malware family classification
US10033759B1 (en) 2015-09-28 2018-07-24 Fireeye, Inc. System and method of threat detection under hypervisor control
US10210329B1 (en) 2015-09-30 2019-02-19 Fireeye, Inc. Method to detect application execution hijacking using memory protection
US9825976B1 (en) 2015-09-30 2017-11-21 Fireeye, Inc. Detection and classification of exploit kits
US9825989B1 (en) 2015-09-30 2017-11-21 Fireeye, Inc. Cyber attack early warning system
US10284575B2 (en) 2015-11-10 2019-05-07 Fireeye, Inc. Launcher for setting analysis environment variations for malware detection
US10341363B1 (en) 2015-12-28 2019-07-02 Fireeye, Inc. Dynamically remote tuning of a malware content detection system
US10133866B1 (en) 2015-12-30 2018-11-20 Fireeye, Inc. System and method for triggering analysis of an object for malware in response to modification of that object
US9824216B1 (en) 2015-12-31 2017-11-21 Fireeye, Inc. Susceptible environment detection system
US10019280B2 (en) * 2016-03-25 2018-07-10 Intel Corporation Technologies for dynamically managing data bus bandwidth usage of virtual machines in a network device
US20170277554A1 (en) * 2016-03-25 2017-09-28 Intel Corporation Technologies for dynamically managing data bus bandwidth usage of virtual machines in a network device
US10169585B1 (en) 2016-06-22 2019-01-01 Fireeye, Inc. System and methods for advanced malware detection through placement of transition events
US10341365B1 (en) 2016-06-30 2019-07-02 Fireeye, Inc. Methods and system for hiding transition events for malware detection
US10335738B1 (en) 2018-09-24 2019-07-02 Fireeye, Inc. System and method for detecting time-bomb malware

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