US20080244742A1 - Detecting adversaries by correlating detected malware with web access logs - Google Patents

Detecting adversaries by correlating detected malware with web access logs Download PDF

Info

Publication number
US20080244742A1
US20080244742A1 US11/824,718 US82471807A US2008244742A1 US 20080244742 A1 US20080244742 A1 US 20080244742A1 US 82471807 A US82471807 A US 82471807A US 2008244742 A1 US2008244742 A1 US 2008244742A1
Authority
US
United States
Prior art keywords
security
endpoint
compromised
enterprise network
assessment
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Abandoned
Application number
US11/824,718
Other languages
English (en)
Inventor
John Neystadt
Efim Hudis
Yair Helman
Alexandra Faynburd
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Microsoft Technology Licensing LLC
Original Assignee
Microsoft Corp
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Microsoft Corp filed Critical Microsoft Corp
Priority to US11/824,718 priority Critical patent/US20080244742A1/en
Priority to EP08744348A priority patent/EP2142996A1/fr
Priority to PCT/US2008/058189 priority patent/WO2008124295A1/fr
Assigned to MICROSOFT CORPORATION reassignment MICROSOFT CORPORATION ASSIGNMENT OF ASSIGNORS INTEREST (SEE DOCUMENT FOR DETAILS). Assignors: FAYNBURD, ALEXANDRA, HUDIS, EFIM, HELMAN, YAIR, NEYSTADT, JOHN
Publication of US20080244742A1 publication Critical patent/US20080244742A1/en
Assigned to MICROSOFT TECHNOLOGY LICENSING, LLC reassignment MICROSOFT TECHNOLOGY LICENSING, LLC ASSIGNMENT OF ASSIGNORS INTEREST (SEE DOCUMENT FOR DETAILS). Assignors: MICROSOFT CORPORATION
Abandoned legal-status Critical Current

Links

Images

Classifications

    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L63/00—Network architectures or network communication protocols for network security
    • H04L63/14—Network architectures or network communication protocols for network security for detecting or protecting against malicious traffic
    • H04L63/1408—Network architectures or network communication protocols for network security for detecting or protecting against malicious traffic by monitoring network traffic
    • H04L63/1425—Traffic logging, e.g. anomaly detection
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L63/00—Network architectures or network communication protocols for network security
    • H04L63/30—Network architectures or network communication protocols for network security for supporting lawful interception, monitoring or retaining of communications or communication related information
    • H04L63/308—Network architectures or network communication protocols for network security for supporting lawful interception, monitoring or retaining of communications or communication related information retaining data, e.g. retaining successful, unsuccessful communication attempts, internet access, or e-mail, internet telephony, intercept related information or call content

Definitions

  • An enterprise computing environment is an organization of any size that uses computers and operates a local area network connected to the Internet.
  • an enterprise computing environment includes a number of client computing devices and one or more servers.
  • Various types of security products including but not limited to firewall products, anti-malware products, intrusion detection/prevention products, reputation service products, and the like are available to protect client- and server-based operating systems and other applications of the enterprise computing environment from security threats.
  • malware includes but is not limited to viruses, Trojan horses, worms, spyware, rootkits, phishing attacks, and other malicious software that generally originates from a malicious presence on the Internet, such as a hacker's Web site.
  • viruses Trojan horses
  • worms worms
  • spyware rootkits
  • phishing attacks and other malicious software that generally originates from a malicious presence on the Internet, such as a hacker's Web site.
  • hackers use to compromise client computing devices is by seducing users to download and execute malware from what appear to be legitimate Web sites.
  • Individual security products often operate in isolation, providing localized security solutions for enterprise computing environments. Deploying and maintaining a wide variety of individual security products is generally expensive and complicated. In addition, individual security products can suffer from various performance problems such as: high rates of false-positives or false-negatives; limited use of automatic responses; overly localized responses; delayed responses; limited access to contextual data desirable to assess security threats; and static data collection policies that result in the collection or retention of too little or too much data.
  • An automated arrangement for detecting adversaries is provided by examining a log that contains records of communications in to and out of the enterprise network upon the detection of a security incident by which a host computer on an enterprise network becomes compromised.
  • the log is analyzed over a window of time starting before the occurrence of the detected security incident to identify the web site URIs (Uniform Resource Identifiers) and IP (Internet Protocol) addresses (collectively “resources”) that were respectively accessed by the compromised host and/or from which traffic was received by the compromised host.
  • URIs Uniform Resource Identifiers
  • IP Internet Protocol
  • the security incident is detected by an antivirus security product that may be configured to operate on individual host computers, or as a centralized product.
  • the communication logs are maintained by another security product such as a firewall, proxy server, router, or gateway (hereinafter collectively referred to as a firewall) that monitors traffic across the network boundary.
  • the correlation is performed by an analyzer that is either configured as a standalone malware analysis security product, or through incorporation of such functionality into the firewall or the antivirus security product.
  • Each security product, called an endpoint is arranged to share security-related information over a common communication channel using an abstraction called a security assessment.
  • a security assessment is generally configured to indicate an endpoint's understanding of a security incident, and is published into the channel and received by subscribing endpoints to enable security-related information to be shared.
  • the antivirus security detects a compromised host computer, it publishes a security assessment into the channel which the malware analyzer receives to thereby trigger the analysis and correlation.
  • a specialized endpoint is utilized that operates as a central audit point that subscribes to all security assessments and logs them.
  • An alert is generated to an administrator in the enterprise when the correlation results in a suspected malicious resource.
  • the administrator can review the correlation data from the malware analyzer, the firewall logs, the antivirus logs, and the security assessment logs, and make a determination as to whether to block the suspicious web site or IP address that is identified in the alert.
  • the administrator may optimize the correlation to improve its accuracy or sensitivity, for example, by adjusting the size of the time window, or by adjusting the threshold number of hosts that must become compromised (by accessing the same resource) before that resource is deemed suspicious and an alert is raised.
  • FIG. 1 is a simplified functional block diagram of an architecture for distributed security in an enterprise computing environment
  • FIG. 2 is a simplified functional block diagram of the security assessment system shown in FIG. 1 ;
  • FIG. 3 is a message sequence chart illustrating certain aspects of methods for handling security threats to the enterprise computing environment shown in FIG. 1 ;
  • FIG. 4 is a simplified functional block diagram of an exemplary configuration of an operating environment in which the security assessment system shown in FIG. 2 may be implemented or used.
  • SEs Security endpoints
  • SEs receive and share security assessments via a common communication channel.
  • a security assessment is defined as a tentative assignment by an SE of broader contextual meaning to information (i.e., data in some context) that is collected about an object of interest in the environment such as a computer, user, service (e.g., a website), external IP address, data, or the enterprise as a whole.
  • the security assessment utilizes a concise vocabulary for an SE to declare that an object in the environment falls into a particular assessment category such as “compromised” or “under attack” along with the severity (e.g., low, medium, high, critical) of the detected incident.
  • a security assessment is tentative because it is subject to some uncertainty and is valid for a limited period of time.
  • the tentative nature of a security assessment is reflected in two of its components: a fidelity field which expresses the level of confidence the SE has in its assignment of contextual meaning, and a time-to-live (“TTL”) field which reflects the endpoint's estimate of the time period for which the security assessment is expected to be valid.
  • TTL time-to-live
  • a security assessment may be used by an SE to declare, in light of that endpoint's current understanding of one or more security incidents, that a particular machine is compromised, with a critical level of severity, with medium fidelity, and having a TTL of 30 minutes.
  • a variety of security assessment types may be used in any given enterprise security environment including those having for example, various combinations of assessment category and object types.
  • SEs are enabled with functionality to publish security assessments onto the common communication channel operating in the environment, as well as subscribe to a subset of available security assessments published by other SEs.
  • the security assessments existing in the environment that are active i.e., those having a TTL which indicates the assessments are still valid
  • the SE then makes a decision as to what local action or response is appropriate for each type of security assessment (whether received from another endpoint or internally generated by the endpoint itself) in accordance with a set of response policies.
  • Incident detection is both efficient and cost-effective because the security context enables distributed processing of enterprise-wide information, in the form of security assessments, without the burden of sharing large amounts of raw data throughout the enterprise (most of which is completely irrelevant due to the lack of any context). SEs are further arranged to roll-back the local action upon expiration of the security assessment that prompted the local action (i.e., when the security assessment exceeds the time-to-live specified in the TTL field).
  • a security assessment system facilitates distributed management of, and response, to security incidents in an enterprise computing environment that includes a number of client computing devices and a variety of security endpoints. Aspects of the SAS are configured for operation in connection with various SEs. Typically, SEs are specialized security products such as firewall products, anti-malware products, intrusion detection/prevention products, and reputation service products. At least one SE (which may or may not be a specialized security product) is referred to as the security assessment endpoint (“SAE”).
  • SAE security assessment endpoint
  • An SAE performs as a centralized audit point by subscribing to all security assessments, logging the security assessments, and also logging the local actions taken by SEs in response to security incidents in the environment.
  • the SAE provides administrators with a comprehensive view of the history and current status of the enterprise as a whole and of each individual SE.
  • SEs process the collected security data using security assessment criteria to detect security incidents and identify threats to the security of the enterprise computing system, and generate time-based security assessments that identify specific security incidents.
  • the security assessments are transmitted to other SEs via the common communication channel.
  • SEs respond to applicable security assessments in various ways (such as by taking local action, collecting forensic evidence, and/or generating/transmitting new security data).
  • Virtually unlimited security assessment criteria and combinations thereof such as rules, policies, locally available security data, active security assessments, windows of time, and algorithms), which may be predetermined or determined dynamically, may be used to identify security incidents and responses thereto.
  • the security assessment system facilitates detection of a malicious presence on either a web site or from an Internet Protocol (“IP”) address that poses a threat to the enterprise computing environment or causes a security incident, for example, such as an infection of a computer by a virus.
  • IP Internet Protocol
  • the SAS facilitates the detection of a malware-compromised client computing device within the enterprise computing environment.
  • the SAS enables automatic collection of forensic evidence upon identification of a particular security incident to the enterprise computing environment.
  • FIG. 1 is a simplified block diagram of an architecture 100 that includes SAS 101 (discussed in detail in connection with FIG. 2 ), which facilitates distributed management of security incidents in enterprise computing environment (“ECE”) 102 .
  • SAS 101 discussed in detail in connection with FIG. 2
  • ECE enterprise computing environment
  • malware 105 examples include but are not limited to a malicious Web site presence or IP address 103 , and malware 105 , which may compromise client- and server-based operating systems and other applications within ECE 102 . Although malware 105 is generally depicted as originating from presence 103 , malware 105 may originate from any source.
  • ECE 102 represents an organization of any size that uses computers and operates a local area network (“LAN”) 120 connected to the Internet 125 .
  • LAN 120 is a wireless or wired network that facilitates the transmission or receipt of information within a relatively small physical area surrounding a device or an entity such as a person or a business (generally, up to a few hundred meters), using any communication protocol or technique.
  • LAN 120 is an Intranet.
  • ECE 102 includes: a number of client computing devices 130 ( 1 through N devices are depicted) that optionally have access to one or more functions of SAS 101 ; one or more security servers 140 upon which a number of security endpoints (“SEs”) 145 (three SEs are depicted, SE 1 146 , SE 2 147 , and SE 3 148 ) having access to one or more functions of SAS 101 are implemented; and one or more servers 150 upon which other functions of ECE 102 (such as Web access, email, file transfer protocol functions, etc.) are implemented. It will be appreciated that servers 140 and 150 may be the same server(s) or different servers.
  • SEs security endpoints
  • servers 140 and 150 may be the same server(s) or different servers.
  • Client computing devices 130 include any portable or non-portable electronic devices or components thereof that are configured for operation within LAN(s) 120 by users 111 .
  • Examples of client computing devices 130 include but are not limited to personal electronic devices such as PCs, fixed-purpose networked devices, or software applications running on general- or special/fixed-purpose computers.
  • SEs 145 represent any hardware, software, firmware, or combination thereof configured to protect ECE 102 from security threats. Generally, SEs function as both sources and collectors of security assessments, which are shared via a common communication channel (“CCC”) 160 within LAN 120 .
  • CCC 160 is any physical or logical technology, protocol, or technique for transmitting data between computing devices. Examples of CCC 160 include but are not limited to buses, messages, data, addresses, and other devices or signals.
  • SEs are specialized security products such as firewall products, anti-malware products, intrusion detection/prevention products, reputation service products, and the like (as shown, SE 1 146 and SE 2 147 are specialized security products).
  • At least one SE includes the functions of a security assessment endpoint (“SAE”) 161 (as shown, SE 3 includes SAE functions 161 , which are discussed further in connection with FIGS. 2 and 3 below) which facilitates the centralized data logging and audit point in the ECE 102 .
  • SAE security assessment endpoint
  • FIG. 2 is a simplified functional block diagram of security assessment system (“SAS”) 101 , aspects of which are usable with SEs 145 and/or client computing devices 130 to facilitate management of security threats in ECE 102 .
  • SAS security assessment system
  • SAS 101 includes: a communication manager 202 ; a security assessment and response engine 240 ; and information repository(ies) 208 , which may be implemented using various types and arrangements of computer-readable media 404 (discussed further below, in connection with FIG. 4 ), that represent data storage capability for information relating to management of security threats within ECE 102 .
  • Information storable within information repository(ies) 208 includes but is not limited to: security data 162 ; security assessments 170 ; and security assessment criteria 220 .
  • SAS 101 In general, design choices and operating environments dictate how specific functions SAS 101 are implemented. Particular configurations of SAS 101 may include fewer, more, or different components than those described. Aspects of SAS 101 may be implemented using hardware, software, or firmware, or combinations thereof. Functions of ECE 102 may operate at any layer of a communication protocol stack, such as at any layer of the well-known stack that defines internetworking: layer 1 , the Physical Layer; layer 2 , the Data Link Layer; layer 3 , the Network Layer; layer 4 , the Transport Layer; layer 5 , the Session Layer; layer 6 , the Presentation Layer; and layer 7 , the Application Layer.
  • a communication protocol stack such as at any layer of the well-known stack that defines internetworking: layer 1 , the Physical Layer; layer 2 , the Data Link Layer; layer 3 , the Network Layer; layer 4 , the Transport Layer; layer 5 , the Session Layer; layer 6 , the Presentation Layer; and layer 7 , the Application Layer.
  • SAS 101 begins with further details about information relating to management of security threats that is storable within information repository(ies) 208 and sharable via CCC.
  • Security data 162 is information in any form or format generated or collected by a particular SE 145 or client computing device 130 of ECE 102 for the purpose of identifying or responding to security threats.
  • security data 162 is in the form of a data structure having predetermined fields populated by information generated by a particular SE or client computing device. It is possible for security data 162 from different sources to have disparate formats. In this case, it may be desirable to transcribe (either at the time of generation or collection) such security data 162 to a common format, to facilitate the collection, evaluation, and storage of relevant security data 162 by SAS 101 in various operating environments. Transcription of security data 162 is not discussed in detail herein.
  • Specialized security products 146 and 147 generate security data 162 , both during normal operation and in response to security assessments 170 (discussed further below).
  • Specific security products generate certain (often different) kinds of security data 162 , which is generally periodically transmitted via CCC 160 in accordance with security assessment criteria 220 (discussed further below).
  • a firewall product generates one kind of security data 162 representing logs of attempts by client computing devices 130 to access Internet resources such as Web sites (such logs generally include records of uniform resource identifiers (“URIs”) associated with the resources); an anti-malware product generates another kind of security data 162 detailing infections of particular client computing devices 130 with malware 105 ; and a reputation service product generates yet another kind of security data 162 , which is generally information about particular malicious resources accessible via the Internet.
  • URIs uniform resource identifiers
  • SE 148 that includes SAE function 161 , periodically collects security data 162 transmitted via CCC 160 from various sources, and evaluates the collected security data in accordance with security assessment criteria 220 to identify security incidents. Upon identification of security incidents, SE 148 /SAE function 161 transmits security assessments 170 via CCC 160 . Security assessments 170 include information in any form or format transmitted for the purpose of identifying security incidents.
  • security assessments 170 are in the form of data structures having predetermined fields populated by information generated by SAE function 161 .
  • Security assessment criteria 220 represent any information usable for decision-making regarding identification of or in response to security incidents within ECE 102 .
  • security assessment criteria 220 may be used by one or more components of SAS 101 to determine: what security data 162 or security assessments 170 are generated or collected; when to generate or collect security data 162 or security assessments 170 ; how to evaluate and respond to collected security data 162 or security assessments 170 ; and/or to which devices within ECE 102 to transmit security data 162 or security assessments 170 .
  • Security assessment criteria 220 may be received from an administrator (not shown) or user 111 , pre-programmed into or dynamically determined by SAS 101 , communicated via CCC 160 , or received from a third party (for example, a local or remote service).
  • Virtually unlimited security assessment criteria 220 and combinations thereof are possible.
  • expressions designed to filter security data 162 or security assessments 170 based on rules, policies, statistical algorithms, locally available security data, sources, recipients, temporal references (such as times, dates, windows of time, and the like), or device-related parameters (such as available memory, processing capabilities, user identities, and the like), among other things, may be created and evaluated in connection with various functions of SAS 101 .
  • communication manager 202 includes one or more physical or logical elements, such as connectivity devices or computer-executable instructions, which enable intra- or inter-device communication via CCC 160 .
  • information sharing agent 242 facilitates communication of security data 162 and security assessments 170 via CCC 160 between SASs 101 located in various SEs 145 and client computing devices 130 . Communication may be initiated by information sharing agent 242 in any operating environment. Data push or pull techniques may be employed. Asynchronous messaging paradigms such as “pub/sub” may be supported.
  • communication manager 202 /information sharing agent 242 are responsible for the receipt, transmission, and processing of information by a particular device or component thereof, as such information traverses any layer of communication protocols associated with any known or later developed communication model.
  • An exemplary communication model is the well-known abstract model that defines internetworking.
  • Security assessment and response engine (“SARE”) 240 is responsible for using security assessment criteria 220 to handle (generate, collect, or respond to) security data 162 and/or security assessments 170 received via CCC 160 /information sharing agent 242 .
  • SARE 240 In the operating environment of SE 148 that includes SAE functions 161 , SARE 240 is responsible for collecting and evaluating security data 162 from various sources, and generating security assessments 170 .
  • SARE 240 may respond to security assessments 170 in various ways, such as by taking local action, collecting forensic evidence, and/or generating and transmitting new security data 162 . Operation of SARE 240 is also discussed further below, in connection with FIG. 3 .
  • FIG. 3 is a message sequence chart 300 illustrating certain methods for handling security incidents within an enterprise computing environment, such as ECE 102 , using a distributed security assessment system, such as SAS 101 .
  • SAS 101 a distributed security assessment system
  • SEs 145 including several specialized security products, which, as shown, include a firewall product 301 , an anti-malware product 302 , an intrusion detection/prevention product 303 , and a reputation service product 304 .
  • Aspects of SAS 101 are also implemented in an SE that includes SAE function 161 .
  • Client computing devices 130 that implement aspects of SAS 101 are also depicted.
  • SASs 101 within ECE 102 are configured for communication via CCC 160 , and it is assumed that individual information sharing agents 242 possess device addresses, port numbers, and the like, useable to accomplish the transmission and reception of the messaging described herein via CCC 160 .
  • Two exemplary security incidents are discussed—a malicious presence on the Web (or an IP address), and a malware-compromised client computing device.
  • Internet access requests 310 are generated by various client computing devices 130 .
  • Internet access requests 310 are any requests for access to resources (such as Web sites and other resources) accessible via the Internet or another network outside of LAN 120 .
  • resources such as Web sites and other resources
  • Such resources generally have associated URIs.
  • One or more security endpoints 301 , 302 , 303 , and 304 may be configured to handle Internet access requests 310 .
  • Security data generation asterisks 312 represent activities relating to generation of security data 162 by specialized security products 301 , 302 , 303 , and 304 during normal operation, which is transmitted via CCC 160 .
  • Security assessment criteria 220 may be used to determine what security data 162 is generated, and when the security data is transmitted via CCC 160 .
  • Exemplary kinds of security data 162 generated during operation of various specialized security products include but are not limited to: by firewall product 301 , records of URIs associated with Internet access requests 310 ; by anti-malware product 302 , details about infections of particular client computing devices with malware 105 ; by intrusion detection/prevention product 303 , information about intrusions into LAN 120 by malicious presence(s) 103 ; and by reputation service product 304 , information about particular malicious resources accessible via the Internet.
  • Security data evaluation asterisk 314 represents activity by SAE function 161 (generally performed by SARE 240 implemented in the operating environment of SE 148 ) relating to the use of security assessment criteria 220 to evaluate security data 162 collected from specialized security products 301 , 302 , 303 , and 304 via CCC 160 , for the purpose of generating security assessments 170 , which are also transmitted/received via CCC 160 .
  • certain security assessment criteria 220 are configured to periodically (for example, every few hours or any other desirable amount of time) identify security data 162 generated by reputation service product 304 that indicates that a particular Internet-accessible resource poses a security threat to ECE 102 . It is contemplated that such threats can come from malicious web sites or IP addresses. Thus, for example, the reputation service 304 regularly produces an updated list of newly categorized malicious resources (e.g., URIs and IP addresses) which can be included, in some implementations, as part of a security assessment that is shared over the CCC 160 or otherwise communicated.
  • URIs and IP addresses newly categorized malicious resources
  • various responses may be invoked by the receiving SEs or the SAE including, for example, raising an alert to an administrator that one or more resources have been newly categorized, triggering a scan by an anti-virus/malware detecting SE of the client computers in the ECE 102 to look generally for possible systems of infection or compromise (or look for a specific piece of malware), or quarantining or otherwise isolating one or more client computers until a more complete investigation can be completed.
  • a malware analyzer which as noted above can be a standalone SE, or incorporated into an SE having anti-virus/malware detection capability, or incorporated into the reputation service, will analyze the firewall logs to identify, in a retroactive manner over some predetermined time window, those client computers or users in the ECE 102 that had any past communications with the newly categorized resource. That is, communications with a URI or IP address are examined which occurred in the past before the reputation of that URI or IP address was changed. When there is an identified past communication that matches an entry on the list from the reputation service, a security assessment is launched into the CCC 160 which will identify the client computer as being suspected of being compromised. Other SEs in the ECE 102 can then use the security assessment to thereby invoke one or more local responses as noted above.
  • malware analyzer may involve the analysis of a large amount of data (depending on the size of the ECE 102 , and the size of the retroactive time window selected) as well as use bandwidth to receive the reputation data
  • other methodologies may be employed by the malware analyzer. These include a methodology where the firewall logs are retroactively analyzed responsively to an access of a particular resource that has been identified as malicious. This could occur, for example, when a first client accessed a web site a month ago, and a second client attempt to access the same site again today. In this example, it is assumed that a reputation service has flagged the site as having a changed categorization to malicious in between the first and subsequent accesses.
  • firewall log is scanned to identify all past access to that particular URI or IP address by clients or users in the ECE 102 and if identified, additional security assessments will be generated and used to trigger responses by the SEs or SAE.
  • This methodology typically reduces the amount of log scanning and analysis that is performed, but may miss some possible suspected past access to malicious resources because the reputation data being utilized is more limited.
  • more sensitivity is obtained at the expense of more involvement by an administrator to handle alerts.
  • more bandwidth use will be accepted to have more complete reputation data on hand when performing a log analysis.
  • the specific balance selected may be dynamically varied in some cases to tailor the effectiveness of the solution to a particular problem at hand.
  • certain security assessment criteria 220 are configured to identify security data 162 generated by anti-malware product 302 that indicates that a particular client computing device has been infected with malware 105 , and to identify a time window prior to the client computing device becoming infected (for example, five minutes or another amount of time). Additional security assessment criteria 220 are configured to identify a subset of security data 162 generated by firewall product 301 , such as web access logs or logs indicating communications from external IP addresses, during the identified amount of time.
  • Further security assessment criteria 220 are used to identify one or more attempts by the infected client computing device and/or other client computing devices to access a particular URI identified by firewall product 301 .
  • a URI that was accessed by a certain number of client computing devices that then became compromised may be identified and not accessed by any other client, otherwise popular resources such as news sites that are frequently accessed by all clients will be mistakenly identified as malicious (what is termed a “false positive”).
  • One or more security assessments 170 that indicate that the identified URI represents a malicious presence on the Web can then be issued by an SE and used to raise an alert to an administrator, or trigger responses (discussed further below) by one or more specialized security products.
  • the particular size of the time window and the particular number of computers that needs to be compromised through common access to a resource will generally be dependent on circumstances surrounding a specific deployment of the present arrangement. For example, it is generally desirable to establish some degree of time proximity of the contact with the suspected resource and the detection of a security incident that gave rise to the compromise. It is recognized that increasing the size of the time window will result in more mistakes—both an increase in false positives and false negatives (i.e., when a malicious resource is missed as being malicious). A time period that is too short will likely weaken the causal link between the communication and the security and result in more false negatives.
  • security assessments response asterisks 316 represent activities relating to determining/performing an appropriate response to security assessments 170 by specialized security products 301 , 302 , 303 , and 304 .
  • Examples of responses include but are not limited to taking local action (such as scanning for malware), collecting forensic evidence, and/or generating and transmitting new security data 162 .
  • Security assessment criteria 220 may be used to specify instructions for obtaining security assessments 170 via CCC 160 , such as whether security assessments 170 are pushed to or pulled from a particular location, and security assessment criteria 220 may also be used to ascertain and/or implement an appropriate response to a security assessment received via CCC 160 . It is generally desirable to identify an amount of time, such as a window of time, in which responses to security assessments 170 are performed. Outside of the window of time, normal operation of specialized security products generally resumes. One exemplary amount of time is a window of time based on (for example, beginning at) the time when a particular security assessment 170 was received.
  • forensic evidence is collected within a certain window of time, such as the window of time beginning when a security assessment 170 is received until a predetermined end time (such as an hour).
  • Examples of forensic evidence include but are not limited to: network traffic captures, hard disk data, transaction contents, more detailed logs such as firewall logs and audit logs associated with an operating system, and memory dumps.
  • Such forensic evidence might have been unavailable to forensic investigators arriving days or weeks after the detection of the security incident because of the high cost of maintaining large amounts of data.
  • conventional static policies that are applied to the collection of forensic evidence usually specify that evidence is retained for relatively short periods of time using either a time-based policy (i.e., data is dumped from the evidence store after “X” hours, days weeks, etc. on a first-in-first-out (“FIFO”) basis) or storage-based policy (i.e., data is dumped from a fixed size storage medium, file or partition of “Y” megabytes or gigabytes, etc. such as a disk or array on a FIFO basis).
  • time-based policy i.e., data is dumped from the evidence store after “X” hours, days weeks, etc. on a first-in-first-out (“FIFO”) basis
  • storage-based policy i.e., data is dumped from a fixed size storage medium, file or partition of “Y” megabytes or gigabytes, etc. such as a disk or array on a FIFO basis.
  • Objects in the environment include objects which are internal to the ECE 102 such as client computers, users, and network subnets (e.g., network branches, separate buildings in the ECE, etc.). Objects may also typically include those that are external to the ECE 102 including IP addresses and web site URIs, for example.
  • an SE upon detecting a security incident, an SE will publish a security assessment into the CCC 160 that describes the incident, and the object to which it applies, along with severity, fidelity, TTL, etc.
  • the detecting SE if so capable, will begin collection of relevant forensic evidence that is associated with the object.
  • those SEs that are capable of collecting forensic evidence will also start to do so.
  • the starting time of the collection will coincide with the detection of the event, or receipt of the security assessment.
  • any forensic evidence that may have relevance to the security incident that an SE may have already collected prior to the detection will be marked for retention so that it is not otherwise deleted through operation of normal policies.
  • the SEs that perform the forensic evidence collection will typically switch to some form of data collection that is more detailed than that routinely performed (i.e., in the absence of a security incident).
  • Such detailed data collection may include, for example, more comprehensive event logging, collecting details regarding content of transactions in the environment (e.g., at the packet level), capturing network requests, and capturing network activities.
  • the dynamic policies use the context from the security assessment to identify specific objects of interest for which forensic evidence is collected and retained, and determine what kinds of evidence is collected, for how long it is collected, and the length of time it is retained. For example, forensic evidence relating to a particular object such as a computer that is suspected of being severely compromised by a rootkit may be kept longer than for other non-compromised objects and all activities may be logged for that compromised object. By comparison, forensic evidence about an object for which there is no particular suspicion will be kept for shorter period of time, and perhaps only network activities are logged. The impact on the enterprise is therefore bounded and the forensic evidence that is collected has increased likelihood of being meaningful
  • the fidelity, severity, or category of a security assessment pertaining to the object may be other criteria that are considered in a particular dynamic forensic evidence retention policy. For example, higher fidelity assessments may result in longer evidence retention as compared with other objects where the applicable security assessments have lower fidelity. Similarly, security assessments having high or critical severity may result in longer retention, or different types, or more extensive forensic evidence being collected.
  • the policies are dynamic to take into account that the security environment is itself subject to change. Reputations may change, new malware developed, web sites put up and taken down, etc. and the security assessments being shared in the ECE 102 are inherently structured to account for such changes. Therefore, for example, if a particular security assessment having low severity and low fidelity is received by an SE, in light of that SE's information about the object of interest, the SE may generate a new security assessment having high severity with high fidelity. The collection and retention policies for forensic evidence for the object of interest may be changed to reflect the new security assessment.
  • the particular SE that detects a security incident about a particular object can be different than the SE which collects the forensic evidence about the object.
  • an SE that implements an anti-virus product might detect that an email contains some malicious code that infected a client computer.
  • the anti-virus SE sends out a security assessment that is received by an SE that implements a firewall or perimeter security product which then begins to log more detailed activity by the infected computer in accordance with a policy that keeps the logged data on hand for a longer period of time than for objects that have not been compromised.
  • security assessment(s) 170 may trigger responses in several places.
  • the infected client computing device may be manually or automatically scanned/cleaned, and firewall, anti-malware, intrusion detection/prevention, and reputation service products 301 , 302 , 303 , and 304 , respectively, may generate new security data 162 and/or take other action, such as collecting forensic evidence.
  • one or more security assessment(s) 170 that identify a malicious URI may trigger responses by one or more specialized security products.
  • Firewall, anti-malware, intrusion detection/prevention, and reputation service products 301 , 302 , 303 , and 304 may generate new security data 162 and/or take other action, such as collecting forensic evidence.
  • the method(s) illustrated via FIG. 3 may be implemented using computer-executable instructions executable by one or more general, multi-purpose, or single-purpose processors (exemplary computer-executable instructions 406 and processor 402 are discussed further below, in connection with FIG. 4 ).
  • exemplary computer-executable instructions 406 and processor 402 are discussed further below, in connection with FIG. 4 .
  • the methods described herein are not constrained to a particular order or sequence.
  • some of the described method(s) or steps thereof can occur or be performed concurrently. It will further be understood that all of the steps shown need not occur in performance of the functions described herein—the type, quantity, and implementation of specific messaging is a matter of implementation preference.
  • FIG. 4 is a block diagram of an exemplary configuration of an operating environment 400 (such as a client computing device or a server) in which all or part of SAS 101 and/or the methods shown and discussed in connection with FIG. 3 may be implemented or used.
  • Operating environment 400 is generally indicative of a wide variety of general-purpose or special-purpose computing environments, and is not intended to suggest any limitation as to the scope of use or functionality of the system(s) and methods described herein.
  • the operating environment 400 includes processor 402 , computer-readable media 404 , and computer-executable instructions 406 .
  • One or more internal buses 420 which are widely available elements, may be used to carry data, addresses, control signals, and other information within, to, or from operating environment 400 or elements thereof.
  • Processor 402 which may be a real or a virtual processor, controls functions of operating environment 400 by executing computer-executable instructions 406 .
  • Processor 402 may execute instructions 406 at the assembly, compiled, or machine-level to perform a particular process.
  • Computer-readable media 404 represent any number and combination of local or remote devices, in any form, now known or later developed, capable of recording, storing, or transmitting computer-readable data, such as computer-executable instructions 406 , security assessments 170 , security assessment criteria 220 , or security data 162 .
  • computer-readable media 404 may be, or may include, a semiconductor memory (such as a read only memory (“ROM”), any type of programmable ROM (“PROM”), a random access memory (“RAM”), or a flash memory, for example); a magnetic storage device (such as a floppy disk drive, a hard disk drive, a magnetic drum, a magnetic tape, or a magneto-optical disk); an optical storage device (such as any type of compact disk or digital versatile disk); a bubble memory; a cache memory; a core memory; a holographic memory; a memory stick; a paper tape; a punch card; or any combination thereof.
  • Computer-readable media 404 may also include transmission media and data associated therewith. Examples of transmission media/data include, but are not limited to, data embodied in any form of wireline or wireless transmission, such as packetized or non-packetized data carried by a modulated carrier signal.
  • Computer-executable instructions 406 represent any signal processing methods or stored instructions. Generally, computer-executable instructions 406 are implemented as software components according to well-known practices for component-based software development, and encoded in computer-readable media (such as computer-readable media 404 ). Computer programs may be combined or distributed in various ways. Computer-executable instructions 406 , however, are not limited to implementation by any specific embodiments of computer programs, and in other instances may be implemented by, or executed in, hardware, software, firmware, or any combination thereof.
  • certain computer-executable instructions 406 implement security assessment and response functions 440 , which implement aspects of security assessment and response engine 240 (shown in FIG. 2 ); and certain computer-executable instructions 406 implement assessment sharing functions 442 , which implement aspects of assessment sharing agent 242 .
  • Input interface(s) 416 are physical or logical elements that facilitate receipt of input to operating environment 400 . Input may be received using any type of now known or later-developed physical or logical elements, such as user interfaces, remote controls, displays, mice, pens, styluses, trackballs, keyboards, microphones, scanning devices, and all types of devices that are used to input data.
  • Output interface(s) 418 are physical or logical elements that facilitate provisioning of output from operating environment 400 .
  • Output may be provided using any type of now known or later-developed physical or logical elements, such as user interfaces, displays, printers, speakers, disk drives, and the like.
  • Network interface(s) 210 represent one or more physical or logical elements, such as connectivity devices or computer-executable instructions that enable communication by operating environment 400 via one or more protocols or techniques (such as via CCC 160 ). Information received at a given network interface may traverse one or more of the seven vertical layers of the OSI Intemetworking Model (or any other applicable communication protocol model).
  • Specialized hardware 414 represents any hardware or firmware that implements functions of operating environment 400 .
  • Examples of specialized communication hardware 414 include encoder/decoders (“CODECs”), application-specific integrated circuits, and the like.
  • operating environment 400 or SAS 101 may include fewer, more, or different components or functions than those described.
  • functional components of operating environment 400 or SAS 101 may be implemented by one or more devices, which are co-located or remotely located, in a variety of ways.
  • connections depicted herein may be logical or physical in practice to achieve a coupling or communicative interface between elements. Connections may be implemented, among other ways, as inter-process communications among software processes, or inter-machine communications among networked computers.

Landscapes

  • Engineering & Computer Science (AREA)
  • Computer Security & Cryptography (AREA)
  • Computer Hardware Design (AREA)
  • Computing Systems (AREA)
  • General Engineering & Computer Science (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
  • Technology Law (AREA)
  • Computer And Data Communications (AREA)
  • Data Exchanges In Wide-Area Networks (AREA)
  • Information Transfer Between Computers (AREA)
US11/824,718 2007-04-02 2007-06-30 Detecting adversaries by correlating detected malware with web access logs Abandoned US20080244742A1 (en)

Priority Applications (3)

Application Number Priority Date Filing Date Title
US11/824,718 US20080244742A1 (en) 2007-04-02 2007-06-30 Detecting adversaries by correlating detected malware with web access logs
EP08744348A EP2142996A1 (fr) 2007-04-02 2008-03-26 Détection d'adversaires par corrélation de logiciel malveillant détecté à des journaux d'accès web
PCT/US2008/058189 WO2008124295A1 (fr) 2007-04-02 2008-03-26 Détection d'adversaires par corrélation de logiciel malveillant détecté à des journaux d'accès web

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
US90970607P 2007-04-02 2007-04-02
US11/824,718 US20080244742A1 (en) 2007-04-02 2007-06-30 Detecting adversaries by correlating detected malware with web access logs

Publications (1)

Publication Number Publication Date
US20080244742A1 true US20080244742A1 (en) 2008-10-02

Family

ID=39796645

Family Applications (3)

Application Number Title Priority Date Filing Date
US11/824,649 Active 2029-10-16 US7882542B2 (en) 2007-04-02 2007-06-30 Detecting compromised computers by correlating reputation data with web access logs
US11/824,732 Active 2030-07-09 US8424094B2 (en) 2007-04-02 2007-06-30 Automated collection of forensic evidence associated with a network security incident
US11/824,718 Abandoned US20080244742A1 (en) 2007-04-02 2007-06-30 Detecting adversaries by correlating detected malware with web access logs

Family Applications Before (2)

Application Number Title Priority Date Filing Date
US11/824,649 Active 2029-10-16 US7882542B2 (en) 2007-04-02 2007-06-30 Detecting compromised computers by correlating reputation data with web access logs
US11/824,732 Active 2030-07-09 US8424094B2 (en) 2007-04-02 2007-06-30 Automated collection of forensic evidence associated with a network security incident

Country Status (3)

Country Link
US (3) US7882542B2 (fr)
EP (2) EP2142996A1 (fr)
WO (3) WO2008124295A1 (fr)

Cited By (19)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20080229419A1 (en) * 2007-03-16 2008-09-18 Microsoft Corporation Automated identification of firewall malware scanner deficiencies
US20090126005A1 (en) * 2007-11-08 2009-05-14 Min Sik Kim Method, apparatus and system for managing malicious-code spreading sites using firewall
US20100154059A1 (en) * 2008-12-11 2010-06-17 Kindsight Network based malware detection and reporting
US20110225650A1 (en) * 2010-03-11 2011-09-15 Accenture Global Services Limited Systems and methods for detecting and investigating insider fraud
US20130262621A1 (en) * 2012-03-29 2013-10-03 Andrei Yoryevich Sherbakov Telecommunication system with variable functionality
US20140040279A1 (en) * 2012-08-02 2014-02-06 International Business Machines Corporation Automated data exploration
US8826302B2 (en) * 2012-11-02 2014-09-02 Airbus Operations (S.A.S.) Methods, systems and computer readable media for establishing a communication link between software simulation models
US8910284B1 (en) * 2010-06-30 2014-12-09 Amazon Technologies, Inc. Detecting malware
US9083730B2 (en) 2013-12-06 2015-07-14 At&T Intellectual Property I., L.P. Methods and apparatus to identify an internet protocol address blacklist boundary
US20150326601A1 (en) * 2014-05-10 2015-11-12 Informatica Corporation Assessment type-variable enterprise security impact analysis
US20160065600A1 (en) * 2014-09-02 2016-03-03 Electronics And Telecommunications Research Institute Apparatus and method for automatically detecting malicious link
US9853940B2 (en) 2015-09-24 2017-12-26 Microsoft Technology Licensing, Llc Passive web application firewall
US20190036945A1 (en) * 2014-11-06 2019-01-31 Palantir Technologies Inc. Malicious software detection in a computing system
US10484407B2 (en) 2015-08-06 2019-11-19 Palantir Technologies Inc. Systems, methods, user interfaces, and computer-readable media for investigating potential malicious communications
CN111031025A (zh) * 2019-12-07 2020-04-17 杭州安恒信息技术股份有限公司 一种自动化检测验证Webshell的方法及装置
US10805321B2 (en) 2014-01-03 2020-10-13 Palantir Technologies Inc. System and method for evaluating network threats and usage
US10826933B1 (en) * 2016-03-31 2020-11-03 Fireeye, Inc. Technique for verifying exploit/malware at malware detection appliance through correlation with endpoints
US10834051B2 (en) 2013-04-08 2020-11-10 Amazon Technologies, Inc. Proxy server-based malware detection
US10893059B1 (en) 2016-03-31 2021-01-12 Fireeye, Inc. Verification and enhancement using detection systems located at the network periphery and endpoint devices

Families Citing this family (99)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2007050244A2 (fr) 2005-10-27 2007-05-03 Georgia Tech Research Corporation Procede et systeme pour detecter et reagir a des attaques de reseaux
US8533283B2 (en) 2007-05-03 2013-09-10 Gary Stephen Shuster Redirection method for electronic content
US8875272B2 (en) * 2007-05-15 2014-10-28 International Business Machines Corporation Firewall for controlling connections between a client machine and a network
US10027688B2 (en) 2008-08-11 2018-07-17 Damballa, Inc. Method and system for detecting malicious and/or botnet-related domain names
US8484739B1 (en) * 2008-12-15 2013-07-09 Symantec Corporation Techniques for securely performing reputation based analysis using virtualization
US20100162240A1 (en) * 2008-12-23 2010-06-24 Samsung Electronics Co., Ltd. Consistent security enforcement for safer computing systems
US8631080B2 (en) * 2009-03-12 2014-01-14 Microsoft Corporation Email characterization
WO2010111715A2 (fr) * 2009-03-27 2010-09-30 Kuity Corp. Méthodologies, outils et processus d'analyse des menaces assurance information en matière de sourçage et d'approvisionnement
US8280968B1 (en) * 2009-04-20 2012-10-02 The Florida State University Research Foundation, Inc. Method of detecting compromised computers in a network
US8336100B1 (en) * 2009-08-21 2012-12-18 Symantec Corporation Systems and methods for using reputation data to detect packed malware
WO2011035378A1 (fr) * 2009-09-23 2011-03-31 Elexon Electronics Pty Ltd Logement de repère d'identification radiofréquence
FI20096394A0 (fi) * 2009-12-23 2009-12-23 Valtion Teknillinen Tunkeutumisen havaitseminen viestintäverkoissa
US8578497B2 (en) 2010-01-06 2013-11-05 Damballa, Inc. Method and system for detecting malware
US8826438B2 (en) 2010-01-19 2014-09-02 Damballa, Inc. Method and system for network-based detecting of malware from behavioral clustering
US8474039B2 (en) 2010-01-27 2013-06-25 Mcafee, Inc. System and method for proactive detection and repair of malware memory infection via a remote memory reputation system
US8955131B2 (en) 2010-01-27 2015-02-10 Mcafee Inc. Method and system for proactive detection of malicious shared libraries via a remote reputation system
US20110185428A1 (en) * 2010-01-27 2011-07-28 Mcafee, Inc. Method and system for protection against unknown malicious activities observed by applications downloaded from pre-classified domains
US8819826B2 (en) 2010-01-27 2014-08-26 Mcafee, Inc. Method and system for detection of malware that connect to network destinations through cloud scanning and web reputation
US9544328B1 (en) * 2010-03-31 2017-01-10 Trend Micro Incorporated Methods and apparatus for providing mitigations to particular computers
US8627475B2 (en) 2010-04-08 2014-01-07 Microsoft Corporation Early detection of potential malware
US20110302655A1 (en) * 2010-06-08 2011-12-08 F-Secure Corporation Anti-virus application and method
US9246932B2 (en) 2010-07-19 2016-01-26 Sitelock, Llc Selective website vulnerability and infection testing
US9147071B2 (en) 2010-07-20 2015-09-29 Mcafee, Inc. System and method for proactive detection of malware device drivers via kernel forensic behavioral monitoring and a back-end reputation system
US9516058B2 (en) 2010-08-10 2016-12-06 Damballa, Inc. Method and system for determining whether domain names are legitimate or malicious
US9536089B2 (en) 2010-09-02 2017-01-03 Mcafee, Inc. Atomic detection and repair of kernel memory
US20120143650A1 (en) * 2010-12-06 2012-06-07 Thomas Crowley Method and system of assessing and managing risk associated with compromised network assets
US8631489B2 (en) 2011-02-01 2014-01-14 Damballa, Inc. Method and system for detecting malicious domain names at an upper DNS hierarchy
US9117074B2 (en) 2011-05-18 2015-08-25 Microsoft Technology Licensing, Llc Detecting a compromised online user account
CN102158499B (zh) * 2011-06-02 2013-09-18 国家计算机病毒应急处理中心 基于http流量分析的挂马网站检测方法
US9087324B2 (en) 2011-07-12 2015-07-21 Microsoft Technology Licensing, Llc Message categorization
US9065826B2 (en) * 2011-08-08 2015-06-23 Microsoft Technology Licensing, Llc Identifying application reputation based on resource accesses
US9160539B1 (en) * 2011-09-30 2015-10-13 Emc Corporation Methods and apparatus for secure, stealthy and reliable transmission of alert messages from a security alerting system
US9922190B2 (en) 2012-01-25 2018-03-20 Damballa, Inc. Method and system for detecting DGA-based malware
US20130227352A1 (en) * 2012-02-24 2013-08-29 Commvault Systems, Inc. Log monitoring
US8978137B2 (en) * 2012-02-29 2015-03-10 Cisco Technology, Inc. Method and apparatus for retroactively detecting malicious or otherwise undesirable software
US8850588B2 (en) 2012-05-01 2014-09-30 Taasera, Inc. Systems and methods for providing mobile security based on dynamic attestation
US9659175B2 (en) * 2012-05-09 2017-05-23 SunStone Information Defense Inc. Methods and apparatus for identifying and removing malicious applications
US9043920B2 (en) 2012-06-27 2015-05-26 Tenable Network Security, Inc. System and method for identifying exploitable weak points in a network
US9027138B2 (en) 2012-06-29 2015-05-05 Centurylink Intellectual Property Llc Identification of infected devices in broadband environments
US9088606B2 (en) 2012-07-05 2015-07-21 Tenable Network Security, Inc. System and method for strategic anti-malware monitoring
US10547674B2 (en) 2012-08-27 2020-01-28 Help/Systems, Llc Methods and systems for network flow analysis
US9894088B2 (en) 2012-08-31 2018-02-13 Damballa, Inc. Data mining to identify malicious activity
US9680861B2 (en) 2012-08-31 2017-06-13 Damballa, Inc. Historical analysis to identify malicious activity
US10084806B2 (en) 2012-08-31 2018-09-25 Damballa, Inc. Traffic simulation to identify malicious activity
US20150373040A1 (en) * 2013-01-31 2015-12-24 Hewlett-Packard Development Company, L.P. Sharing information
US9268940B1 (en) * 2013-03-12 2016-02-23 Symantec Corporation Systems and methods for assessing internet addresses
US8931093B2 (en) * 2013-03-15 2015-01-06 Bank Of America Corporation Malicious request attribution
US9467464B2 (en) 2013-03-15 2016-10-11 Tenable Network Security, Inc. System and method for correlating log data to discover network vulnerabilities and assets
US9578045B2 (en) * 2013-05-03 2017-02-21 Webroot Inc. Method and apparatus for providing forensic visibility into systems and networks
US9571511B2 (en) 2013-06-14 2017-02-14 Damballa, Inc. Systems and methods for traffic classification
US9438626B1 (en) * 2013-06-18 2016-09-06 Emc Corporation Risk scoring for internet protocol networks
US9443075B2 (en) 2013-06-27 2016-09-13 The Mitre Corporation Interception and policy application for malicious communications
US9456003B2 (en) 2013-07-24 2016-09-27 At&T Intellectual Property I, L.P. Decoupling hardware and software components of network security devices to provide security software as a service in a distributed computing environment
RU2645265C2 (ru) * 2013-12-05 2018-02-19 Закрытое акционерное общество "Лаборатория Касперского" Система и способ блокировки элементов интерфейса приложения
US9386041B2 (en) * 2014-06-11 2016-07-05 Accenture Global Services Limited Method and system for automated incident response
US9794279B2 (en) * 2014-06-11 2017-10-17 Accenture Global Services Limited Threat indicator analytics system
US10447733B2 (en) 2014-06-11 2019-10-15 Accenture Global Services Limited Deception network system
US10819717B2 (en) * 2014-11-14 2020-10-27 Nippon Telegraph And Telephone Corporation Malware infected terminal detecting apparatus, malware infected terminal detecting method, and malware infected terminal detecting program
US9888031B2 (en) * 2014-11-19 2018-02-06 Cyber Secdo Ltd. System and method thereof for identifying and responding to security incidents based on preemptive forensics
US9774613B2 (en) 2014-12-15 2017-09-26 Sophos Limited Server drift monitoring
US9571512B2 (en) 2014-12-15 2017-02-14 Sophos Limited Threat detection using endpoint variance
US9419989B2 (en) * 2014-12-15 2016-08-16 Sophos Limited Threat detection using URL cache hits
RU2606556C2 (ru) * 2015-02-20 2017-01-10 Закрытое акционерное общество "Лаборатория Касперского" Способ ввода конфиденциальных данных
US9930065B2 (en) 2015-03-25 2018-03-27 University Of Georgia Research Foundation, Inc. Measuring, categorizing, and/or mitigating malware distribution paths
US9680844B2 (en) 2015-07-06 2017-06-13 Bank Of America Corporation Automation of collection of forensic evidence
WO2017027003A1 (fr) 2015-08-10 2017-02-16 Hewlett Packard Enterprise Development Lp Évaluation du comportement d'un système
US10664590B2 (en) 2015-10-01 2020-05-26 Twistlock, Ltd. Filesystem action profiling of containers and security enforcement
US10943014B2 (en) 2015-10-01 2021-03-09 Twistlock, Ltd Profiling of spawned processes in container images and enforcing security policies respective thereof
US10223534B2 (en) 2015-10-15 2019-03-05 Twistlock, Ltd. Static detection of vulnerabilities in base images of software containers
US10567411B2 (en) 2015-10-01 2020-02-18 Twistlock, Ltd. Dynamically adapted traffic inspection and filtering in containerized environments
US10586042B2 (en) 2015-10-01 2020-03-10 Twistlock, Ltd. Profiling of container images and enforcing security policies respective thereof
US10922418B2 (en) 2015-10-01 2021-02-16 Twistlock, Ltd. Runtime detection and mitigation of vulnerabilities in application software containers
US10706145B2 (en) 2015-10-01 2020-07-07 Twistlock, Ltd. Runtime detection of vulnerabilities in software containers
US10599833B2 (en) 2015-10-01 2020-03-24 Twistlock, Ltd. Networking-based profiling of containers and security enforcement
US10778446B2 (en) 2015-10-15 2020-09-15 Twistlock, Ltd. Detection of vulnerable root certificates in software containers
US20170149810A1 (en) * 2015-11-25 2017-05-25 Hewlett Packard Enterprise Development Lp Malware detection on web proxy log data
GB2545486B (en) * 2015-12-18 2019-12-11 F Secure Corp Evasive intrusion detection in private network
US9992216B2 (en) 2016-02-10 2018-06-05 Cisco Technology, Inc. Identifying malicious executables by analyzing proxy logs
US10798111B2 (en) * 2016-09-14 2020-10-06 International Business Machines Corporation Detecting intrusion attempts in data transmission sessions
US10771492B2 (en) * 2016-09-22 2020-09-08 Microsoft Technology Licensing, Llc Enterprise graph method of threat detection
US9830469B1 (en) 2016-10-31 2017-11-28 International Business Machines Corporation Automated mechanism to secure customer data
US10346625B2 (en) 2016-10-31 2019-07-09 International Business Machines Corporation Automated mechanism to analyze elevated authority usage and capability
US9928365B1 (en) 2016-10-31 2018-03-27 International Business Machines Corporation Automated mechanism to obtain detailed forensic analysis of file access
US10650156B2 (en) 2017-04-26 2020-05-12 International Business Machines Corporation Environmental security controls to prevent unauthorized access to files, programs, and objects
US10554678B2 (en) 2017-07-26 2020-02-04 Cisco Technology, Inc. Malicious content detection with retrospective reporting
US10735457B2 (en) * 2017-10-03 2020-08-04 Microsoft Technology Licensing, Llc Intrusion investigation
IL258345B2 (en) * 2018-03-25 2024-01-01 B G Negev Technologies And Applications Ltd At Ben Gurion Univ – 907553 A rapid framework for ensuring cyber protection, inspired by biological systems
US11245667B2 (en) 2018-10-23 2022-02-08 Akamai Technologies, Inc. Network security system with enhanced traffic analysis based on feedback loop and low-risk domain identification
US11283827B2 (en) 2019-02-28 2022-03-22 Xm Cyber Ltd. Lateral movement strategy during penetration testing of a networked system
US11100064B2 (en) 2019-04-30 2021-08-24 Commvault Systems, Inc. Automated log-based remediation of an information management system
US11206281B2 (en) 2019-05-08 2021-12-21 Xm Cyber Ltd. Validating the use of user credentials in a penetration testing campaign
CN110376957B (zh) * 2019-07-04 2020-09-25 哈尔滨工业大学（威海） 一种基于安全规约自动构建的plc安全事件取证方法
US11245703B2 (en) 2019-09-27 2022-02-08 Bank Of America Corporation Security tool for considering multiple security contexts
US11115369B1 (en) 2020-07-29 2021-09-07 Motorola Solutions, Inc. Transmitting near real-time geographic mass messaging requests
US11176024B1 (en) 2020-09-23 2021-11-16 International Business Machines Corporation Software patch application and testing optimization
US11799904B2 (en) * 2020-12-10 2023-10-24 Cisco Technology, Inc. Malware detection using inverse imbalance subspace searching
CN112769804A (zh) * 2020-12-31 2021-05-07 北京恒光信息技术股份有限公司 互联网安全监管方法、系统、计算机设备及可读存储介质
US11954065B2 (en) 2022-03-18 2024-04-09 Motorola Solutions, Inc. Device and method for extending retention periods of records
IT202200010355A1 (it) * 2022-05-18 2023-11-18 Kopjra S R L Procedimento e sistema per l'acquisizione automatica forense del contenuto di pagine web.

Citations (55)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5983270A (en) * 1997-03-11 1999-11-09 Sequel Technology Corporation Method and apparatus for managing internetwork and intranetwork activity
US6353385B1 (en) * 2000-08-25 2002-03-05 Hyperon Incorporated Method and system for interfacing an intrusion detection system to a central alarm system
US6530024B1 (en) * 1998-11-20 2003-03-04 Centrax Corporation Adaptive feedback security system and method
US20030051163A1 (en) * 2001-09-13 2003-03-13 Olivier Bidaud Distributed network architecture security system
US20030126449A1 (en) * 2001-12-28 2003-07-03 Kelly Nicholas Paul Controlling access to suspicious files
US20030131256A1 (en) * 2002-01-07 2003-07-10 Ackroyd Robert John Managing malware protection upon a computer network
US20030159069A1 (en) * 2002-02-19 2003-08-21 Byeong Cheol Choi Network-based attack tracing system and method using distributed agent and manager system
US20030208689A1 (en) * 2000-06-16 2003-11-06 Garza Joel De La Remote computer forensic evidence collection system and process
US20040098623A1 (en) * 2002-10-31 2004-05-20 Secnap Network Security, Llc Intrusion detection system
US20040111643A1 (en) * 2002-12-02 2004-06-10 Farmer Daniel G. System and method for providing an enterprise-based computer security policy
US20040255167A1 (en) * 2003-04-28 2004-12-16 Knight James Michael Method and system for remote network security management
US20040260733A1 (en) * 2003-06-23 2004-12-23 Adelstein Frank N. Remote collection of computer forensic evidence
US20050015626A1 (en) * 2003-07-15 2005-01-20 Chasin C. Scott System and method for identifying and filtering junk e-mail messages or spam based on URL content
US20050033989A1 (en) * 2002-11-04 2005-02-10 Poletto Massimiliano Antonio Detection of scanning attacks
US20050050318A1 (en) * 2003-07-30 2005-03-03 International Business Machines Corporation Profiled access to wireless LANs
US20050080816A1 (en) * 2003-04-25 2005-04-14 Messagelabs Limited Method of, and system for, heurisically determining that an unknown file is harmless by using traffic heuristics
US20050102537A1 (en) * 2003-11-07 2005-05-12 Sony Corporation File transfer protocol for mobile computer
US20050102534A1 (en) * 2003-11-12 2005-05-12 Wong Joseph D. System and method for auditing the security of an enterprise
US6925443B1 (en) * 2000-04-26 2005-08-02 Safeoperations, Inc. Method, system and computer program product for assessing information security
US20050204169A1 (en) * 2004-03-10 2005-09-15 Tonnesen Steven D. System and method for detection of aberrant network behavior by clients of a network access gateway
US20050251570A1 (en) * 2002-04-18 2005-11-10 John Heasman Intrusion detection system
US20050257267A1 (en) * 2003-02-14 2005-11-17 Williams John L Network audit and policy assurance system
US20050268112A1 (en) * 2004-05-28 2005-12-01 Microsoft Corporation Managing spyware and unwanted software through auto-start extensibility points
US20050289649A1 (en) * 2004-05-27 2005-12-29 Fujitsu Limited Malicious access-detecting apparatus, malicious access-detecting method, malicious access-detecting program, and distributed denial-of-service attack-detecting apparatus
US20060018466A1 (en) * 2004-07-12 2006-01-26 Architecture Technology Corporation Attack correlation using marked information
US20060031938A1 (en) * 2002-10-22 2006-02-09 Unho Choi Integrated emergency response system in information infrastructure and operating method therefor
US20060070130A1 (en) * 2004-09-27 2006-03-30 Microsoft Corporation System and method of identifying the source of an attack on a computer network
US20060075494A1 (en) * 2004-10-01 2006-04-06 Bertman Justin R Method and system for analyzing data for potential malware
US7028338B1 (en) * 2001-12-18 2006-04-11 Sprint Spectrum L.P. System, computer program, and method of cooperative response to threat to domain security
US20060123478A1 (en) * 2004-12-02 2006-06-08 Microsoft Corporation Phishing detection, prevention, and notification
US7065657B1 (en) * 1999-08-30 2006-06-20 Symantec Corporation Extensible intrusion detection system
US7093294B2 (en) * 2001-10-31 2006-08-15 International Buisiness Machines Corporation System and method for detecting and controlling a drone implanted in a network attached device such as a computer
US7124438B2 (en) * 2002-03-08 2006-10-17 Ciphertrust, Inc. Systems and methods for anomaly detection in patterns of monitored communications
US20060236392A1 (en) * 2005-03-31 2006-10-19 Microsoft Corporation Aggregating the knowledge base of computer systems to proactively protect a computer from malware
US7134141B2 (en) * 2000-06-12 2006-11-07 Hewlett-Packard Development Company, L.P. System and method for host and network based intrusion detection and response
US20060259968A1 (en) * 2005-05-12 2006-11-16 Hirofumi Nakakoji Log analysis system, method and apparatus
US20060265689A1 (en) * 2002-12-24 2006-11-23 Eugene Kuznetsov Methods and apparatus for processing markup language messages in a network
US20060272011A1 (en) * 2000-06-30 2006-11-30 Internet Security Systems, Inc. Method and apparatus for network assessment and authentication
US20060268112A1 (en) * 2005-05-26 2006-11-30 Sony Corporation Imaging device and method, computer program product on computer-readable medium, and imaging system
US7152242B2 (en) * 2002-09-11 2006-12-19 Enterasys Networks, Inc. Modular system for detecting, filtering and providing notice about attack events associated with network security
US20060294588A1 (en) * 2005-06-24 2006-12-28 International Business Machines Corporation System, method and program for identifying and preventing malicious intrusions
US20070006310A1 (en) * 2005-06-30 2007-01-04 Piccard Paul L Systems and methods for identifying malware distribution sites
US20070016951A1 (en) * 2005-07-13 2007-01-18 Piccard Paul L Systems and methods for identifying sources of malware
US7174566B2 (en) * 2002-02-01 2007-02-06 Intel Corporation Integrated network intrusion detection
US20070101440A1 (en) * 2005-10-17 2007-05-03 Oracle International Corporation Auditing correlated events using a secure web single sign-on login
US7325252B2 (en) * 2001-05-18 2008-01-29 Achilles Guard Inc. Network security testing
US20080046556A1 (en) * 2002-09-16 2008-02-21 Geoffrey Deane Owen Nicholls Method and apparatus for distributed rule evaluation in a near real-time business intelligence system
US7346922B2 (en) * 2003-07-25 2008-03-18 Netclarity, Inc. Proactive network security system to protect against hackers
US7530104B1 (en) * 2004-02-09 2009-05-05 Symantec Corporation Threat analysis
US7558848B1 (en) * 2004-02-27 2009-07-07 F5 Networks, Inc. System and method for determining integrity over a virtual private network tunnel
US7614085B2 (en) * 2002-05-09 2009-11-03 Protegrity Corporation Method for the automatic setting and updating of a security policy
US7644271B1 (en) * 2005-11-07 2010-01-05 Cisco Technology, Inc. Enforcement of security policies for kernel module loading
US7647622B1 (en) * 2005-04-22 2010-01-12 Symantec Corporation Dynamic security policy through use of empirical security events
US7661136B1 (en) * 2005-12-13 2010-02-09 At&T Intellectual Property Ii, L.P. Detecting anomalous web proxy activity
US7793338B1 (en) * 2004-10-21 2010-09-07 Mcafee, Inc. System and method of network endpoint security

Family Cites Families (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR100424724B1 (ko) 2001-07-27 2004-03-27 김상욱 네트워크 흐름 분석에 의한 침입 탐지 장치
KR100441409B1 (ko) 2001-11-12 2004-07-23 주식회사 안철수연구소 바이러스 탐지 엔진을 갖는 침입 탐지 시스템
KR100466798B1 (ko) 2001-12-29 2005-01-17 (주)대정아이앤씨 내·외부망 통합 보안 시스템 및 방법
US7290275B2 (en) * 2002-04-29 2007-10-30 Schlumberger Omnes, Inc. Security maturity assessment method
US6952779B1 (en) * 2002-10-01 2005-10-04 Gideon Cohen System and method for risk detection and analysis in a computer network
WO2006047163A2 (fr) 2004-10-26 2006-05-04 Priderock, L.L.C. Systeme et procede d'identification et d'elimination de maliciel dans un systeme informatique
JP2007013263A (ja) * 2005-06-28 2007-01-18 Fujitsu Ltd ワーム判定プログラム、ワーム判定方法およびワーム判定装置
US20070028300A1 (en) * 2005-07-28 2007-02-01 Bishop Ellis E System and method for controlling on-demand security

Patent Citations (56)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5983270A (en) * 1997-03-11 1999-11-09 Sequel Technology Corporation Method and apparatus for managing internetwork and intranetwork activity
US6530024B1 (en) * 1998-11-20 2003-03-04 Centrax Corporation Adaptive feedback security system and method
US7065657B1 (en) * 1999-08-30 2006-06-20 Symantec Corporation Extensible intrusion detection system
US6925443B1 (en) * 2000-04-26 2005-08-02 Safeoperations, Inc. Method, system and computer program product for assessing information security
US7134141B2 (en) * 2000-06-12 2006-11-07 Hewlett-Packard Development Company, L.P. System and method for host and network based intrusion detection and response
US20030208689A1 (en) * 2000-06-16 2003-11-06 Garza Joel De La Remote computer forensic evidence collection system and process
US20060272011A1 (en) * 2000-06-30 2006-11-30 Internet Security Systems, Inc. Method and apparatus for network assessment and authentication
US7162649B1 (en) * 2000-06-30 2007-01-09 Internet Security Systems, Inc. Method and apparatus for network assessment and authentication
US6353385B1 (en) * 2000-08-25 2002-03-05 Hyperon Incorporated Method and system for interfacing an intrusion detection system to a central alarm system
US7325252B2 (en) * 2001-05-18 2008-01-29 Achilles Guard Inc. Network security testing
US20030051163A1 (en) * 2001-09-13 2003-03-13 Olivier Bidaud Distributed network architecture security system
US7093294B2 (en) * 2001-10-31 2006-08-15 International Buisiness Machines Corporation System and method for detecting and controlling a drone implanted in a network attached device such as a computer
US7028338B1 (en) * 2001-12-18 2006-04-11 Sprint Spectrum L.P. System, computer program, and method of cooperative response to threat to domain security
US20030126449A1 (en) * 2001-12-28 2003-07-03 Kelly Nicholas Paul Controlling access to suspicious files
US20030131256A1 (en) * 2002-01-07 2003-07-10 Ackroyd Robert John Managing malware protection upon a computer network
US7174566B2 (en) * 2002-02-01 2007-02-06 Intel Corporation Integrated network intrusion detection
US20030159069A1 (en) * 2002-02-19 2003-08-21 Byeong Cheol Choi Network-based attack tracing system and method using distributed agent and manager system
US7124438B2 (en) * 2002-03-08 2006-10-17 Ciphertrust, Inc. Systems and methods for anomaly detection in patterns of monitored communications
US20050251570A1 (en) * 2002-04-18 2005-11-10 John Heasman Intrusion detection system
US7614085B2 (en) * 2002-05-09 2009-11-03 Protegrity Corporation Method for the automatic setting and updating of a security policy
US7152242B2 (en) * 2002-09-11 2006-12-19 Enterasys Networks, Inc. Modular system for detecting, filtering and providing notice about attack events associated with network security
US20080046556A1 (en) * 2002-09-16 2008-02-21 Geoffrey Deane Owen Nicholls Method and apparatus for distributed rule evaluation in a near real-time business intelligence system
US20060031938A1 (en) * 2002-10-22 2006-02-09 Unho Choi Integrated emergency response system in information infrastructure and operating method therefor
US20040098623A1 (en) * 2002-10-31 2004-05-20 Secnap Network Security, Llc Intrusion detection system
US20050033989A1 (en) * 2002-11-04 2005-02-10 Poletto Massimiliano Antonio Detection of scanning attacks
US20040111643A1 (en) * 2002-12-02 2004-06-10 Farmer Daniel G. System and method for providing an enterprise-based computer security policy
US20060265689A1 (en) * 2002-12-24 2006-11-23 Eugene Kuznetsov Methods and apparatus for processing markup language messages in a network
US20050257267A1 (en) * 2003-02-14 2005-11-17 Williams John L Network audit and policy assurance system
US20050080816A1 (en) * 2003-04-25 2005-04-14 Messagelabs Limited Method of, and system for, heurisically determining that an unknown file is harmless by using traffic heuristics
US20040255167A1 (en) * 2003-04-28 2004-12-16 Knight James Michael Method and system for remote network security management
US20040260733A1 (en) * 2003-06-23 2004-12-23 Adelstein Frank N. Remote collection of computer forensic evidence
US20050015626A1 (en) * 2003-07-15 2005-01-20 Chasin C. Scott System and method for identifying and filtering junk e-mail messages or spam based on URL content
US7346922B2 (en) * 2003-07-25 2008-03-18 Netclarity, Inc. Proactive network security system to protect against hackers
US20050050318A1 (en) * 2003-07-30 2005-03-03 International Business Machines Corporation Profiled access to wireless LANs
US20050102537A1 (en) * 2003-11-07 2005-05-12 Sony Corporation File transfer protocol for mobile computer
US20050102534A1 (en) * 2003-11-12 2005-05-12 Wong Joseph D. System and method for auditing the security of an enterprise
US7530104B1 (en) * 2004-02-09 2009-05-05 Symantec Corporation Threat analysis
US7558848B1 (en) * 2004-02-27 2009-07-07 F5 Networks, Inc. System and method for determining integrity over a virtual private network tunnel
US20050204169A1 (en) * 2004-03-10 2005-09-15 Tonnesen Steven D. System and method for detection of aberrant network behavior by clients of a network access gateway
US20050289649A1 (en) * 2004-05-27 2005-12-29 Fujitsu Limited Malicious access-detecting apparatus, malicious access-detecting method, malicious access-detecting program, and distributed denial-of-service attack-detecting apparatus
US20050268112A1 (en) * 2004-05-28 2005-12-01 Microsoft Corporation Managing spyware and unwanted software through auto-start extensibility points
US20060018466A1 (en) * 2004-07-12 2006-01-26 Architecture Technology Corporation Attack correlation using marked information
US20060070130A1 (en) * 2004-09-27 2006-03-30 Microsoft Corporation System and method of identifying the source of an attack on a computer network
US20060075494A1 (en) * 2004-10-01 2006-04-06 Bertman Justin R Method and system for analyzing data for potential malware
US7793338B1 (en) * 2004-10-21 2010-09-07 Mcafee, Inc. System and method of network endpoint security
US20060123478A1 (en) * 2004-12-02 2006-06-08 Microsoft Corporation Phishing detection, prevention, and notification
US20060236392A1 (en) * 2005-03-31 2006-10-19 Microsoft Corporation Aggregating the knowledge base of computer systems to proactively protect a computer from malware
US7647622B1 (en) * 2005-04-22 2010-01-12 Symantec Corporation Dynamic security policy through use of empirical security events
US20060259968A1 (en) * 2005-05-12 2006-11-16 Hirofumi Nakakoji Log analysis system, method and apparatus
US20060268112A1 (en) * 2005-05-26 2006-11-30 Sony Corporation Imaging device and method, computer program product on computer-readable medium, and imaging system
US20060294588A1 (en) * 2005-06-24 2006-12-28 International Business Machines Corporation System, method and program for identifying and preventing malicious intrusions
US20070006310A1 (en) * 2005-06-30 2007-01-04 Piccard Paul L Systems and methods for identifying malware distribution sites
US20070016951A1 (en) * 2005-07-13 2007-01-18 Piccard Paul L Systems and methods for identifying sources of malware
US20070101440A1 (en) * 2005-10-17 2007-05-03 Oracle International Corporation Auditing correlated events using a secure web single sign-on login
US7644271B1 (en) * 2005-11-07 2010-01-05 Cisco Technology, Inc. Enforcement of security policies for kernel module loading
US7661136B1 (en) * 2005-12-13 2010-02-09 At&T Intellectual Property Ii, L.P. Detecting anomalous web proxy activity

Cited By (28)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20080229419A1 (en) * 2007-03-16 2008-09-18 Microsoft Corporation Automated identification of firewall malware scanner deficiencies
US20090126005A1 (en) * 2007-11-08 2009-05-14 Min Sik Kim Method, apparatus and system for managing malicious-code spreading sites using firewall
US20100154059A1 (en) * 2008-12-11 2010-06-17 Kindsight Network based malware detection and reporting
US8578491B2 (en) * 2008-12-11 2013-11-05 Alcatel Lucent Network based malware detection and reporting
US20110225650A1 (en) * 2010-03-11 2011-09-15 Accenture Global Services Limited Systems and methods for detecting and investigating insider fraud
US8868728B2 (en) * 2010-03-11 2014-10-21 Accenture Global Services Limited Systems and methods for detecting and investigating insider fraud
US8910284B1 (en) * 2010-06-30 2014-12-09 Amazon Technologies, Inc. Detecting malware
US20130262621A1 (en) * 2012-03-29 2013-10-03 Andrei Yoryevich Sherbakov Telecommunication system with variable functionality
US20140040279A1 (en) * 2012-08-02 2014-02-06 International Business Machines Corporation Automated data exploration
US8826302B2 (en) * 2012-11-02 2014-09-02 Airbus Operations (S.A.S.) Methods, systems and computer readable media for establishing a communication link between software simulation models
US10834051B2 (en) 2013-04-08 2020-11-10 Amazon Technologies, Inc. Proxy server-based malware detection
US9083730B2 (en) 2013-12-06 2015-07-14 At&T Intellectual Property I., L.P. Methods and apparatus to identify an internet protocol address blacklist boundary
US10193900B2 (en) 2013-12-06 2019-01-29 At&T Intellectual Property I., L.P. Methods and apparatus to identify an internet protocol address blacklist boundary
US10805321B2 (en) 2014-01-03 2020-10-13 Palantir Technologies Inc. System and method for evaluating network threats and usage
US9762603B2 (en) * 2014-05-10 2017-09-12 Informatica Llc Assessment type-variable enterprise security impact analysis
US20150326601A1 (en) * 2014-05-10 2015-11-12 Informatica Corporation Assessment type-variable enterprise security impact analysis
US20160065600A1 (en) * 2014-09-02 2016-03-03 Electronics And Telecommunications Research Institute Apparatus and method for automatically detecting malicious link
US20190036945A1 (en) * 2014-11-06 2019-01-31 Palantir Technologies Inc. Malicious software detection in a computing system
US11496509B2 (en) * 2014-11-06 2022-11-08 Palantir Technologies Inc. Malicious software detection in a computing system
US10728277B2 (en) * 2014-11-06 2020-07-28 Palantir Technologies Inc. Malicious software detection in a computing system
US20200351279A1 (en) * 2014-11-06 2020-11-05 Palantir Technologies Inc. Malicious software detection in a computing system
US10484407B2 (en) 2015-08-06 2019-11-19 Palantir Technologies Inc. Systems, methods, user interfaces, and computer-readable media for investigating potential malicious communications
US9853940B2 (en) 2015-09-24 2017-12-26 Microsoft Technology Licensing, Llc Passive web application firewall
US10826933B1 (en) * 2016-03-31 2020-11-03 Fireeye, Inc. Technique for verifying exploit/malware at malware detection appliance through correlation with endpoints
US10893059B1 (en) 2016-03-31 2021-01-12 Fireeye, Inc. Verification and enhancement using detection systems located at the network periphery and endpoint devices
US11936666B1 (en) 2016-03-31 2024-03-19 Musarubra Us Llc Risk analyzer for ascertaining a risk of harm to a network and generating alerts regarding the ascertained risk
US11979428B1 (en) 2016-03-31 2024-05-07 Musarubra Us Llc Technique for verifying exploit/malware at malware detection appliance through correlation with endpoints
CN111031025A (zh) * 2019-12-07 2020-04-17 杭州安恒信息技术股份有限公司 一种自动化检测验证Webshell的方法及装置

Also Published As

Publication number Publication date
EP2143033A4 (fr) 2017-03-01
US8424094B2 (en) 2013-04-16
US7882542B2 (en) 2011-02-01
US20080244694A1 (en) 2008-10-02
WO2009005925A3 (fr) 2009-06-18
EP2143033B1 (fr) 2021-02-17
WO2008122058A3 (fr) 2009-03-12
EP2143033A2 (fr) 2010-01-13
EP2142996A1 (fr) 2010-01-13
US20080244748A1 (en) 2008-10-02
WO2008122058A2 (fr) 2008-10-09
WO2009005925A2 (fr) 2009-01-08
WO2008124295A1 (fr) 2008-10-16

Similar Documents

Publication Publication Date Title
US7882542B2 (en) Detecting compromised computers by correlating reputation data with web access logs
US11949692B1 (en) Method and system for efficient cybersecurity analysis of endpoint events
US20210250372A1 (en) Peer Device Protection
US10218740B1 (en) Fuzzy hash of behavioral results
US11343265B2 (en) System and methods for malware detection using log analytics for channels and super channels
US9686293B2 (en) Systems and methods for malware detection and mitigation
US9185127B2 (en) Network protection service
Liu et al. Bottracer: Execution-based bot-like malware detection
US8805995B1 (en) Capturing data relating to a threat
Punithavathani et al. Surveillance of anomaly and misuse in critical networks to counter insider threats using computational intelligence
US20160164893A1 (en) Event management systems
US20080229421A1 (en) Adaptive data collection for root-cause analysis and intrusion detection
US11636208B2 (en) Generating models for performing inline malware detection
US11374946B2 (en) Inline malware detection
US20200106791A1 (en) Intelligent system for mitigating cybersecurity risk by analyzing domain name system traffic metrics
US20230403296A1 (en) Analyses and aggregation of domain behavior for email threat detection by a cyber security system
Raftopoulos et al. Detecting, validating and characterizing computer infections in the wild
Pour et al. Sanitizing the IoT cyber security posture: An operational CTI feed backed up by Internet measurements
US11372971B2 (en) Threat control
Farinholt et al. Dark matter: uncovering the DarkComet RAT ecosystem
US20220245249A1 (en) Specific file detection baked into machine learning pipelines
US12074890B2 (en) Network threat prevention
Nechaev et al. Internet botnets: A survey of detection techniques
Pan et al. Special Issue on Big Data Applications in Cyber Security and Threat Intelligence–Part 2

Legal Events

Date Code Title Description
AS Assignment

Owner name: MICROSOFT CORPORATION, WASHINGTON

Free format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNORS:NEYSTADT, JOHN;HUDIS, EFIM;HELMAN, YAIR;AND OTHERS;REEL/FRAME:020700/0476;SIGNING DATES FROM 20070813 TO 20070830

STCB Information on status: application discontinuation

Free format text: ABANDONED -- FAILURE TO RESPOND TO AN OFFICE ACTION

AS Assignment

Owner name: MICROSOFT TECHNOLOGY LICENSING, LLC, WASHINGTON

Free format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNOR:MICROSOFT CORPORATION;REEL/FRAME:034766/0509

Effective date: 20141014