WO2019113492A1 - Détection et atténuation d'attaques par objet d'authentification falsifié au moyen d'une plateforme de cyberdécision avancée - Google Patents

Détection et atténuation d'attaques par objet d'authentification falsifié au moyen d'une plateforme de cyberdécision avancée Download PDF

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Publication number
WO2019113492A1
WO2019113492A1 PCT/US2018/064541 US2018064541W WO2019113492A1 WO 2019113492 A1 WO2019113492 A1 WO 2019113492A1 US 2018064541 W US2018064541 W US 2018064541W WO 2019113492 A1 WO2019113492 A1 WO 2019113492A1
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WIPO (PCT)
Prior art keywords
authentication object
data
network
processor
cryptographic hash
Prior art date
Application number
PCT/US2018/064541
Other languages
English (en)
Inventor
Jason Crabtree
Andrew Sellers
Original Assignee
Fractal Industries, Inc.
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
Priority claimed from US15/837,845 external-priority patent/US11005824B2/en
Application filed by Fractal Industries, Inc. filed Critical Fractal Industries, Inc.
Priority to EP18885538.1A priority Critical patent/EP3721364A4/fr
Priority to CN201880076822.4A priority patent/CN111492360A/zh
Publication of WO2019113492A1 publication Critical patent/WO2019113492A1/fr

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Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
    • G06F21/30—Authentication, i.e. establishing the identity or authorisation of security principals
    • G06F21/31—User authentication
    • G06F21/33—User authentication using certificates
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
    • G06F21/30—Authentication, i.e. establishing the identity or authorisation of security principals
    • G06F21/31—User authentication
    • G06F21/41—User authentication where a single sign-on provides access to a plurality of computers
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
    • G06F21/30—Authentication, i.e. establishing the identity or authorisation of security principals
    • G06F21/45—Structures or tools for the administration of authentication
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
    • G06F21/50—Monitoring users, programs or devices to maintain the integrity of platforms, e.g. of processors, firmware or operating systems
    • G06F21/55—Detecting local intrusion or implementing counter-measures
    • G06F21/554—Detecting local intrusion or implementing counter-measures involving event detection and direct action
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
    • G06F21/60—Protecting data
    • G06F21/64—Protecting data integrity, e.g. using checksums, certificates or signatures

Definitions

  • the disclosure relates to the field of network security, particularly to the detecting and mitigating attacks involving forged authentication objects.
  • SAML Securit Assertion Markup Language
  • SAML uses an identity provider to generate an authentication object in which a user may use to access a plurality of federated service offerings within a domain, without the need to authenticate with each individual service.
  • SAML is a widely used protocol in the art, and used applications such as, but is not limited to, MICROSOFT’S ACTIVE DIRECTORY FEDERATED SERVICES, AZURE AD, QKTA, w3 ⁇ 4b browser single- sign-on, and many cloud service providers (such as AMAZON AWS, AZURE, GOOGLE services, and the like).
  • cloud service providers such as AMAZON AWS, AZURE, GOOGLE services, and the like.
  • a system for detecting and mitigating forged authentication object attacks acts as an external, and non-blocking validation service for existing
  • the system provides services to generate cryptographic hashes to legitimately- generated authentication objects, and also to check incoming authentication objects against a database of cr ptographic hashes of previously-generated authentication objects (and detecting fraudulent SAML-based authentication atempts by detecting attempts whose authentication objects’ cryptographic hashes are not present in the database of authentication object hashes).
  • the system may also allow setting of a plurality of rules to trigger events after certain conditions are satisfied.
  • a system for detecting and mitigating forged authentication object attacks comprising an authentication object inspector comprising at least a processor, a memory, and a plurality of programming instructions stored in the memory and operating on the processor, wherein the programmable instructions, when operating on the processor, cause the processor to: observe a new' authentication object generated by an identity provider, and retrieve the new' authentication object; and a hashing engine comprising at least a processor, a memory, and a plurality of programming instructions stored in the memory ' and operating on tire processor, wherein the programmable instructions, when operating on the processor, cause the processor to: retrieve the new authentication object from the authentication object inspector, calculate a cryptographic hash for tire new authentication object by performing at least a plurality ' of calculations and transformations on the new authentication object, and store tire cryptographic hash for the new authentication object in a data store; wherein, upon observing a subsequent access request to a federated service associated with die identity provider and accompanied by a second authentication object
  • the system further comprises an event-rules- condition engine comprising at least a processor, a memory, and a plurality of programming instructions stored in the memory ' and operating on the processor, wherein the programmable instructions, when operating on die processor, cause the processor to: retrieve at least a predefined event-condition-action rule from the data store upon detection of an invalid authentication object, and execute commands as dictated in the predefined event-condition- action rule.
  • a plurality ' of event-condition-action rules are nested to create a series of circuit breaker checks to midgate unauthorized access dirough the use of a forged authentication object.
  • an administrative user upon detection of an invalid authentication object, an administrative user is notified, and provided with access data associated with the invalid authentication object.
  • access data comprises resources accessed by die owner of the invalid authentication object.
  • at least a portion of the access data comprises blast radius data associated with the owner of the invalid authentication object.
  • a method for generating hashes for new' authentication objects comprising the steps of: (a) receiving a request to calculate a cryptographic hash for an authentication object generated by an identity provider using a hashing engine; (b) generating the cryptographic hash by performing at least a plurality ' of calculations and transformations on the new' authentication object using the hashing engine; and (c) storing the cryptographic hash in a data store.
  • a method for detecting and mitigating forged authentication object attacks comprising the steps of: (a) retrieving an incoming authentication object using an authentication object inspector; (b) sending the incoming authentication to a hashing engine using the authentication object inspector; (c) generating a cryptographic hash by performing at least a plurality of calculations and transformations on the incoming authentication object using the hashing engine; (d) retrieving at least a previous generated cryptographic hash using the hashing engine; and (e) validating the incoming authentication object by at least comparing the cryptographic hash for the incoming
  • Fig. 1A is a diagram of an exemplary architecture of an advanced cyber decision platform according to one aspect.
  • Fig. IB is a diagram showing a typical operation of accessing a service provider that relies on the SAML protocol for authentication.
  • FIG. 1C is a diagram showing a method of cyberattack using a forged AO 140, which may also be referred to as a“golden SAML” attack.
  • FIG. 2 is a block diagram illustrating an exemplary' system architecture for a system for detecting and mitigating forged authentication object attacks according to various embodiments of the invention
  • Fig. 3A is a flow diagram of an exemplary ' function of the business operating system in the detection and mitigation of predetermining factors leading to and steps to mitigate ongoing cyberattacks.
  • Fig. 3B is a process diagram showing a general How of the process used to detect rogue devices and analyze them for threats.
  • Fig. 3C is a process diagram showing a general flow' of the process used to detect and prevent privilege escalation attacks on a network.
  • Fig. 3D is a process diagram showing a general flow' of the process used to manage vulnerabilities associated with patches to network software.
  • FIGs. 4A and 4B are process diagrams showing business operating system functions in use to mitigate cyberattacks.
  • Fig. 5 is a process flow diagram of a method for segmenting cyberattack information to appropriate corporation parties.
  • FIG. 6 is a diagram of an exemplary architecture for a system for rapid predictive analysis of ver large data sets using an actor-driven distributed computational graph, according to one aspect.
  • FIG. 7 is a diagram of an exemplary' architecture for a system for rapid predictive analysis of very large data sets using an actor-driven distributed computational graph, according to one
  • FIG. 8 is a diagram of an exemplary architecture for a system for rapid predictive analysis of very large data sets using an actor-driven distributed computational graph, according to one
  • FIG. 9 is a diagram of an exemplary architecture for a user and entity behavioral analysis system, according to one aspect.
  • Fig. 10 is a flow diagram of an exemplary method for cybersecurity behavioral analytics, according to one aspect.
  • Fig. 11 is a flow diagram of an exemplary method for measuring the effects of cybersecurity attacks, according to one aspect.
  • Fig. 12 is a flow diagram of an exemplary method for continuous cybersecurity monitoring and exploration, according to one aspect.
  • Fig. 13 is a flow' diagram of an exemplary method for mapping a cyber- physical system graph (CPG), according to one aspect.
  • CPG cyber- physical system graph
  • Fig. 14 is a flow diagram of an exemplary method for continuous network resilience scoring, according to one aspect.
  • Fig. 15 is a flow diagram of an exemplary method for cybersecurity privilege oversight, according to one aspect.
  • Fig. 16 is a flow diagram of an exemplary method for cybersecurity risk management, according to one aspect.
  • Fig. 17 is a flow diagram of an exemplar ' method for mitigating compromised credential threats, according to one aspect.
  • Fig. 18 is a flow ' diagram of an exemplary' method for dynamic network and rogue device discovery, according to one aspect,
  • Fig. 19 is a flow ' diagram of an exemplary method for Kerberos“golden ticket” attack and “golden SAML” attack detection, according to one aspect.
  • Fig. 20 is a flow diagram of an exemplary method for risk-based vulnerability and patch management, according to one aspect.
  • Fig. 21 is a flow diagram of an exemplary method for establishing groups of users according to one aspect.
  • Fig. 22 is a flow diagram of an exemplary method for monitoring groups for anomalous behavior, according to one aspect.
  • Fig. 23 is a flow' diagram for an exemplary method for handing a detection of anomalous behavior, according to one aspect.
  • Fig. 24 is a flow' diagram illustrating an exemplary ' method for processing a new user connection, according to one aspect.
  • Fig. 25 is a flow diagram illustrating an exemplary' method for verifying die authenticity of an authentication object, according to one aspect.
  • Fig. 26 is a block diagram illustrating an exemplary hardware architecture of a computing device used in various embodiments of the invention.
  • Fig. 27 is a block diagram illustrating an exemplary logical architecture for a client device, according to various embodiments of the invention.
  • Fig. 28 is a block diagram illustrating an exemplary architectural arrangement of clients, servers, and external services, according to various embodiments of the invention.
  • Fig. 29 is another block diagram illustrating an exemplary' hardware architecture of a computing device used in various embodiments of the invention.
  • the inventor has conceived, and reduced to practice, a system and method for detecting and mitigating forged authentication object attacks.
  • Devices that are in communication with each other need not be in continuous communication with each other, unless expressly specified otherwise.
  • devices that are in communication with each other may communicate directly or indirectly through one or more communication means or intermediaries, logical or physical.
  • steps may' be performed simultaneously despite being described or implied as occurring non- simultaneously (e.g., because one step is described after the other step).
  • the illustration of a process by ' its depiction in a drawing does not imply that the illustrated process is exclusive of other variations and modifications thereto, does not imply that the illustrated process or any of its steps are necessary to one or more of the aspects, and does not imply' that die illustrated process is preferred.
  • steps are generally described once per aspect, but this does not mean they' must occur once, or that they may' only occur once each time a process, method, or algorithm is carried out or executed. Some steps may be omitted in some aspects or some occurrences, or some steps may be executed more than once in a given aspect or occurrence.
  • “graph” is a representation of information and relationships, where each primary unit of information makes up a“node” or“vertex” of the graph and the relationship between two nodes makes up an edge of tire graph.
  • Nodes can be further qualified by the connection of one or more descriptors or“properties” to that node. For example, given the node “fames R,” name information for a person, qualifying properties might be“183 cm tall”,“DOB 08/13/1965” and“speaks English”. Similar to the use of properties to further describe the information in a node, a relationship between two nodes that forms an edge can be qualified using a“label”.
  • transformation pipeline are represented as directed graph with each transformation comprising a node and the output messages between transformations comprising edges.
  • Distributed computational graph stipulates the potential use of non-linear transformation pipelines which are programmatically linearized. Such linearization can result in exponential growth of resource consumption.
  • the most sensible approach to overcome possibility is to introduce new transformation pipelines just as they r are needed, creating only those that are ready to compute.
  • Such method results in transformation graphs which are highly variable in size and node, edge composition as the system processes data streams.
  • transformation graph may assume many shapes and sizes with a vast topography of edge relationships.
  • the examples given were chosen for illustrative purposes only and represent a small number of the simplest of possibilities. These examples should not be taken to define the possible graphs expected as part of operation of the invention
  • transformation is a function performed on zero or more streams of input data which results in a single stream of output which may or may not then be used as input for another transformation. Transformations may comprise any combination of machine, human or machine-human interactions Transformations need not change data that enters them, one example of this type of transformation would be a storage transformation which would receive input and then act as a queue for that data for subsequent transformations. As implied above, a specific transformation may generate output data in the absence of input data. A time stamp serves as a example. In the invention, transformations are placed into pipelines such that the output of one transformation may serve as an input for another. These pipelines can consist of two or more transformations with the number of transformations limited only by the resources of the system.
  • transformation pipelines have been linear with each transformation in the pipeline receiving input from one antecedent and providing output to one subsequent with no branching or iteration.
  • Other pipeline configurations are possible.
  • the invention is designed to permit several of these configurations including, but not limited to: linear, afferent branch, efferent branch and cyclical.
  • A“database” or“data storage subsystem” (these terms may be considered substantially synonymous), as used herein, is a system adapted for the long-term storage, indexing, and retrieval of data, the retrieval typicall being via some sort of querying interface or language
  • “Database” may be used to refer to relational database management systems known in the art, but should not be considered to be limited to such systems.
  • Many alternative database or data storage system technologies have been, and indeed are being, introduced in the art, including but not limited to distributed non-relational data storage systems such as Hadoop, column- oriented databases, in-memory databases, and the like.
  • any data storage architecture may be used according to the aspects.
  • one or more particular data storage needs are described as being satisfied by separate components (for example, an expanded private capital markets database and a configuration database), these descriptions refer to functional uses of data storage systems and do not refer to their physical architecture.
  • any group of data storage systems of databases referred to herein may be included together in a single database management system operating on a single machine, or they may be included in a single database management system operating on a cluster of machines as is known in the art.
  • any ' single database (such as an expanded private capital markets database) may be implemented on a single machine, on a set of machines using clustering technology, on several machines connected by one or more messaging systems known in the art, or in a master/slave arrangement common in the art.
  • A“data context”, as used herein, refers to a set of arguments identifying the location of data. This could be a Rabbit queue, a xsv file in cloud-based storage, or any other such location reference except a single event or record. Activities may pass either events or data contexts to each other for processing. The nature of a pipeline allows for direct information passing between activities, and data locations or files do not need to be predetermined at pipeline start.
  • Each batch activity may ' contain a “source” data context (this may' be a streaming context if the upstream activities are streaming), and a“destination” data context (which is passed to the next activity').
  • Streaming activities may' have an optional“destination” streaming data context (optional meaning: caching/persistence of events vs. ephemeral), though this should not be part of the initial implementation.
  • Fig. 1A is a diagram of an exemplary architecture of an advanced cyber decision platform (ACDP) 100 according to one aspect.
  • CASSANDRATM or RED1STM according to various arrangements.
  • the directed computational graph module 155 retrieves one or more streams of data from a plurality of sources, which includes, but is in no way not limited to, a plurality of physical sensors, network service providers, web based questionnaires and surveys, monitoring of electronic infrastructure, crowd sourcing campaigns, and human input device information.
  • data may be split into two identical streams in a specialized pre-programmed data pipeline 155a, wherein one sub-stream may be sent for batch processing and storage while the other sub-stream may be reformatted for transformation pipeline analysis.
  • the data is then transferred to the general transformer service module 160 for linear data transformation as pail of analysis or the decomposable transformer sendee module 150 for branching or iterative transformations that are part of analysis.
  • the directed computational graph module 155 represents all data as directed graphs where the transformations are nodes and the result messages between transformations edges of tire graph.
  • the high volume web crawling module 115 uses multiple server hosted preprogrammed web spiders, which while autonomously configured are deployed within a web scraping framework 115a of which S CRAPYTM is an example, to identify and retrieve data of interest from web based sources that are not well tagged by conventional web crawling technology.
  • the multiple dimension lime series data store module 120 may receive streaming data from a large plurality of sensors dial may be of several different types.
  • the multiple dimension time series data store module may also store any time series data encountered by the system such as but not limited to enterprise network usage data, component and system logs, performance data, network sendee information captures such as, but not limited to news and financial feeds, and sales and service related customer data.
  • the module is designed to accommodate irregular and high volume surges by dynamically allotting network bandwidth and server processing channels to process the incoming data.
  • Inclusion of programming wrappers for languages examples of which are, but not limited to C++, PERL, PYTHON, and ERLANGTM allows sophisticated programming logic to be added to the default function of the multidimensional time series database 120 without intimate knowledge of the core programming, greatly extending breadth of function.
  • Data retrieved by the multidimensional time series database 120 and the high volume w r eb crawling module 115 may be further analyzed and transformed into task optimized results by the directed
  • computational graph 155 and associated general transformer sendee 150 and decomposable transformer service 160 modules may be sent, often with scripted cuing information determining important vertexes 145a, to the graph stack service module 145 which, employing standardized protocols for converting streams of information into graph
  • the graph stack service module 145 represents data in graphical form influenced by any p e- determined scripted modifications 145a and stores it in a graph-based data store 145b such as GIRAPHTM or a key value pair type data store REDISTM, or RIAKTM, among others, all of which are suitable for storing graph-based information.
  • a graph-based data store 145b such as GIRAPHTM or a key value pair type data store REDISTM, or RIAKTM, among others, all of which are suitable for storing graph-based information.
  • Results of the transformative analysis process may then be combined with further client directives, additional business rules and practices relevant to the analysis and situational information external to tire already available data in die automated planning service module 130 which also runs powerful information theory 130a based predictive statistics functions and machine learning algorithms to allow ' future trends and outcomes to be rapidly forecast based upon the current system derived results and choosing each a plurality of possible business decisions.
  • the automated planning service module 130 may propose business decisions most likely to result is the most favorable business outcome with a usably high level of certainty.
  • the action outcome simulation module 125 with its discrete event simulator programming module 125a coupled with the end user facing observation and state estimation service 140 which is highly scriptable 140b as circumstances require and has a game engine 140a to more realistically ' stage possible outcomes of business decisions under consideration, allows business decision makers to investigate the probable outcomes of choosing one pending course of action over another based upon analysis of the current available data,
  • the Information Assurance department is notified by the system 100 that principal X is using credentials K (Kerberos Principal Key) never used by it before to access service Y.
  • Service Y utilizes these same credentials to access secure data on data store Z.
  • Ad hoc simulations of these traffic patterns are run against the baseline by the action outcome simulation module 125 and its discrete event: simulator 125a which is used here to determine probability space for likelihood of legitimacy.
  • the system 100 based on this data and analysis, was able to detect and recommend mitigation of a cyberattack that represented an existential threat to all business operations, presenting, at the time of the attack, information most needed for an actionable plan to human analysts at multiple levels in the mitigation and remediation effort through use of the observation and state estimation service 140 which had also been specifically ' preprogrammed to handle cybersecurity events 140b.
  • a forged authentication object detection anil mitigation service 910 may be used to detect and mitigate cyberattacks stemming from the use of authentication objects generated by an attacker. Sendee 910 is discussed in further detail below in Fig, 2.
  • the advanced cyber decision platform a specifically
  • a client enterprise continuously monitors a client enterprise’s normal network activity for behaviors such as but not limited to normal risers on ihe network, resources accessed by each user, access permissions of each user, machine to machine traffic on the network, sanctioned external access to the core network and administrative access to the network’s identity and access management servers in conjunction with real-time analytics informing knowledge of cyberattack methodology.
  • the system uses this information for two purposes: First, the advanced computational analytics and simulation capabilities of the system are used to provide immediate disclosure of probable digital access points both at the network periphery and within tire enterprise’s information transfer and trust structure and
  • the advanced cyber decision platform continuously monitors the network in real-time both for types of traffic and through techniques such as deep packet inspection for pre-decided analytically significant deviation in user traffic for indications of known cyberattack vectors such as, but not limited to, ACTIVE DIRECTORYTM / Kerberos pass-the- ticket attack, ACTIVE DIRECTORYTM / Kerberos pass-tlie-hash attack and the related ACTIVE DIRECTORYTM / Kerberos overpass ihe hash attack , ACTIVE DIRECTORYTM / Kerberos Skeleton Key, ACTIVE DIRECTORYTM / Kerberos golden and silver ticket attack, privilege escalation attack, compromised user credentials, ransomware disk atacks, and SAME forged authentication object attack (also may be referred to as golden SAME ⁇ .
  • golden SAME ⁇ also may be referred to as golden SAME ⁇ .
  • the system issues action-focused alert information to all predesignated parties specifically ' tailored to their roles in attack mitigation or remediation and formatted to provide predictive attack modeling based upon historic, current, and contextual attack progression analysis such that human decision makers can rapidly formulate the most effective courses of action at their levels of responsibility in command of the most actionable information with as little distractive data as possible.
  • the system then issues defensive measures in the most actionable form to end the attack with the least possible damage and exposure. All attack data are persistently stored for later forensic analysis.
  • Fig. IB is a diagram showing a typical operation of accessing a service provider that relies on the SAML protocol for authentication 120, as used in the art.
  • a user using a computing device, may request access to a one of a plurality of federated servers, and through the steps listed 121, an AO is generated for the user from an identity provider (IdP). The user may then be granted access to, not only the service that was originally requested, but any trusted partners as well.
  • IdP identity provider
  • Fig. 1C is a diagram showing a method of cyberattack using a forged AO 14-0, which may also be referred to as a“golden SAML” attack, as known in the art.
  • a“golden SAML” attack an attacker, using information acquired from a compromised IdP, may generate his own AO, bypassing the need to authenticate with an IdP. Once the AO has been generated, the attacker may assume the role of any user registered with the IdP, and freely access the service providers. While using various systems and methods disclosed herein may be sufficient, additional measures for detecting and mitigating forged authentication object attacks may be required.
  • FIG. 2 is a block diagram illustrating an exemplary system architecture 900 for a system 910 for detecting and mitigating forged authentication object attacks according to various embodiments of the invention.
  • Architecture 900 may comprise system 910 acting as a non blocking intermediary between a connecting user 920, a plurality of federated sendee providers (SP) 921a-it, an identity provider (IdP) 922, and an administrative user 923.
  • SP federated sendee providers
  • IdP identity provider
  • System 910 may be configured to verifying incoming connections when the user has an AO, and also keeps track of legitimately generated AO’s.
  • System 910 may comprise an AO inspector 911, a hashing engine 912, an event- condition- action (EGA) rules engine 913, and a data store 914,
  • AO inspector 911 may be configured to use faculties of ACDP 100, for example DCG module 155 and associated transformer modules to analyze and process AO’s associated with incoming connections, and observation and state estimation services 140 to monitor connections for incoming AO’s. Incoming AO’s may be retrieved for further analysis by system 910.
  • Hashing engine 912 may be configured to calculate a cryptographic hash for AOs generated by identity provider 922 using functions of ACDP 100, such as DCG module 155, generate a cr ptographic hash for both incoming AO’s (for analysis purposes), and new AO’s created by IdP 922.
  • a one-way hash may be used to allow protecting of sensitive information contained in the AO, but preserving uniqueness of each AO, Generated hashes may be stored in data store 914. Hashing engine may also run a hash check function, used for validating incoming AO’s.
  • EGA rules engine 913 may be used by a network administrator to create and manage EGA rules that may trigger actions and queries in the event of detection of a forged AO. Rules may be for example, tracking and logging the actions of the suspicious user, deferring the suspicious connection, and the like. Rules may be nested to create a complex flow of various conditional checks and actions to create a set of“circuit breaker” checks to further ascertain the connection, or try and resolve the matter automatically before notifying a human network administrator.
  • Data store 914 may be a graph and time-series hybrid database, such as multidimensional time-series data store 120 or data store 112, that stores hashes, EGA rules, log data, and the like, and may be quickly and efficiently queried and processed using ACDP 100.
  • Federated service providers 92 la- .a may comprise a group of trusted service partners that may share a common IdP 922 in which user 920 may wish to access.
  • Federated service providers 921 a-u may be, for instance, se dees employing MICROSOFT’S ACTIVE DIRECTORY FEDERATED SERVICES (AS DS), AZURE AD, OKTA, many web browser single- sign-on (SSO) implementations, cloud service provides (such as, AMAZON AWS, AZURE, and GOOGLE), and the like.
  • Fig. 3A is a flow diagram of an exemplary' function of the business operating system in the detection and mitigation of predetermining factors leading to and steps to mitigate ongoing cyberattacks 200.
  • the system continuously' retrieves network traffic data 201 which may be stored and preprocessed by the multidimensional time series data store 120 and its programming wrappers 120a. All captured data are then analysed to predict the normal usage patterns of network nodes such as internal users, network connected systems and equipment and sanctioned users external to the enterprise boundaries for example off ite employees, contractors and vendors, just to name a few likely participants.
  • network nodes such as internal users, network connected systems and equipment and sanctioned users external to the enterprise boundaries for example off ite employees, contractors and vendors, just to name a few likely participants.
  • normal otlier network traffic may also be known to tliose skilled in the field, the list given is not meant to be exclusive and other possibilities would not fall outside the design of the invention.
  • Analysis of network traffic may include graphical analysis of parameters such as network item to network usage using specifically developed programming in the graphstack service 145, 145a, analysis of usage by each network item may be accomplished by specifically pre-developed algorithms associated with the directed computational graph module 155, general transformer service module 160 and decomposable service module 150, depending on the complexity of tire individual usage profile 201.
  • the invention being predictive as well as aware of known exploits is designed to analyze any ' anomalous network behavior, formulate probable outcomes of the behavior, and to then issue any needed alerts regardless of whether the atack follows a published exploit specification or exhibits novel characteristics deviant to normal network practice.
  • the system then is designed to get needed information to responding parties 206 tailored, where possible, to each role in mitigating the attack and damage arising from it 207. This may include the exact subset of information included in alerts and updates and the format in which the information is presented which may be through the enterprise’s existing security information and event management system.
  • Network administrators might receive information such as but not limited to where on the network the atack is believed to have originated, what systems are believed currently affected, predictive information on where the attack may progress, what enterprise information is at risk and actionable recommendations on repelling the intrusion and mitigating the damage, whereas a chief information security officer may receive alert including but not limited to a timeline of the cyberattack, the services and information believed compromised, what action, if any has been taken to mitigate the attack, a prediction of how' the attack may ' unfold and the recommendations given to control and repel the attack 207, although all parties may access any network and cyberattack information for which they have granted access at any time, unless compromise is suspected. Other specifically tailored updates may be issued by' the system 206, 207.
  • Fig. 3B is a process diagram showing a general How of the process used to detect rogue devices and analyze them for threats 220.
  • the connection is immediately sent to the rogue device detector 222 for analysis.
  • the advanced cyber decision platform uses machine learning algorithms to analyze system-wide data to detect threats.
  • the connected device is analyzed 223 to assess its device type, settings, and capabilities, the sensitivity of the data stored on the server to which the device wishes to connect, network activity, server logs, remote queries, and a multitude of other data to determine the level of threat associated with the device. If the threat reaches a certain level 224, the device is automatically prevented from accessing the network 225, and the system
  • the device is allowed to connect to the network 227.
  • Fig. 3C is a process diagram showing a general flow' of the process used to detect and prevent privilege escalation attacks on a network (for example,“Golden Ticket” attacks or “golden SAML” attacks) 240.
  • a network for example,“Golden Ticket” attacks or “golden SAML” attacks
  • the connection is immediately sent to the privilege escalation attack detector 242 for analysis.
  • the advanced cyber decision platform uses machine learning algorithms to analyze system-wide data to detect threats.
  • the access request is analyzed 243 to assess the validity of the access request using the digital signature validation, plus other system-wide information such as the sensitivity of the server being accessed, the newness of the digital signature or AO, the digital signature’s or AO’s prior usage, and other measures of the digital signature’s or AO’s validity. If the assessment determines that the access request represents a significant threat 244, even despite the Kerberos validation of the digital signature or validation of a AO, the access request is automatically denied 245, and the system administrator is notified of the potential threat, along with contextually-based, tactical recommendations for optimal response based on potential impact 246. Otherwise, the access request is granted 247.
  • Fig. 3D is a process diagram showing a general flow of the process used to manage vulnerabilities associated with patches to network software 260.
  • data is gathered from both sources external to the network 262 and internal to the network 263.
  • the advanced cyber decision platform uses machine learning algorithms to analyze system- wide data to defect threats. The data is analyzed 264 to determine whether network
  • Figs. 4A and 4B are process diagrams showing a general flow 300 of business operating system functions in use to mitigate cyberattacks.
  • Input network data which may include network flow patterns 321, the origin and destination of each piece of measurable network traffic 322, system logs from servers and workstations on the network 323, endpoint data 323a, any security event log data from servers or available security information and event (SIEM) systems 324, external threat intelligence feeds 324a, identity or assessment context 325, external network health or cybersecurity feeds 326, Kerberos domain controller or ACTIVE DIRECTORYTM server logs or instrumentation 327 and business unit performance related data 328, among many other possible data types for which the invention was designed to analyze and integrate, may pass into 315 the business operating system 310 for analysis as part of its cyber security function.
  • SIEM security information and event
  • These multiple types of data from a plurality of sources may be transformed for analysis 311, 312 using at least one of the specialized cybersecurity, risk assessment or common functions of the business operating system in the role of cybersecurity system, such as, but not limited to network and system user privilege oversight 331, network and system user behavior analytics 332, attacker and defender action timeline 333, SIEM integration and analysis 334, dynamic benchmarking 335, and incident identification and resolution performance analytics 336 among other possible cybersecurity functions; value at risk (VAR) modeling and simulation 341, anticipatory vs.
  • VAR value at risk
  • Output 317 can be used to configure network gateway security appliances 361, to assist in preventing network intrusion through predictive change to infrastructure recommendations 362, to alert an enterprise of ongoing cyberattack early in the attack cycle, possibly thwarting it but at least mitigating the damage 368, to record compliance to standardized guidelines or SLA requirements 363, to continuously probe existing network infrastructure and issue alerts to any r changes which may make a breach more likely 364, suggest solutions to any domain controller ticketing weaknesses detected 365, detect presence of malware 366, and perform one time or continuous vulnerability scanning depending on client directives 367.
  • These examples are, of course, only a subset of the possible uses of the system, they are exemplary in nature and do not reflect any boundaries in the capabilities of tire invention.
  • Fig. 5 is a process flow diagram of a method for segmenting cyberattack information to appropriate corporation parties 400.
  • one of the strengths of the advanced cyberdecision platform is the ability to finely customize reports and dashboards to specific audiences, concurrently is appropriate. This customization is possible due to the devotion of a portion of the business operating system’s programming specifically to outcome presentation by modules which include the observation and state estimation service 140 with its game engine 140a and script interpreter 140b.
  • issuance of specialized alerts, updates and reports may significantly assist in getting the correct mitigating actions done in the most timely fashion while keeping all participants informed at predesignated, appropriate granularity.
  • Examples of groups that may receive specialized information streams include but may not be limited to front line responders during the attack 404, incident forensics support both during and after the attack 405, chief information security officer 406 and chief risk officer 407 the information sent to the latter two focused to appraise overall damage and to implement both mitigating strategy and preventive changes after the attack.
  • Front line responders may use the cyber-decision platform’s analyzed, transformed and correlated information specifically sent to them 404a to probe the extent of tire attack, isolate such things as: the predictive attacker’s entry point onto the enterprise’s network, the systems involved or the predictive ultimate targets of the attack and may use the simulation capabilities of the system to investigate alternate methods of successfully ending the attack and repelling the attackers in the most efficient manner, although many other queries known to those skilled in the art are also answerable by the invention.
  • Simulations run may also include the predictive effects of any attack mitigating actions on normal and critical operation of the enterprise’s IT systems and corporate users.
  • a chief information security officer may use the cyber-decision platform to predictively analyze 406a what corporate information has already been compromised, predictively simulate tire ultimate information targets of the attack that may or may not have been compromised and the total impact of the attack what can be done now and in the near future to safeguard that information.
  • the forensic responder may use the cyber-decision platform 405a to clearly and completely map the extent of network infrastructure through predictive simulation and large volume data analysis.
  • the forensic analyst may also use the platform’s capabilities to perform a time series and infrastructural spatial analysis of the attack’s progression with methods used to infiltrate the enterprise’s subnets and servers. Again, the chief risk officer would perform analyses of what information 407a w r as stolen and predictive simulations on what the theft means to the enterprise as time progresses. Additionally, the system’s predictive capabilities may be employed to assist in creation of a plan for changes of the IT infrastructural that should be made that are optimal for remediation of cybersecurity risk under possibly limited enterprise budgetary' constraints in place at the company so as to maximize financial outcome.
  • Fig. 6 is a diagram of an exemplary ' architecture for a system for rapid predictive analysis of very ' large data sets using an actor-driven distributed computational graph 500, according to one aspect.
  • a DCG 500 may' comprise a pipeline orchestrator 501 that may be used to perform a variety of data transformation functions on data within a processing pipeline, and may be used with a messaging system 510 that enables communication with any' number of various services and protocols, relaying messages and translating them as needed into protocol-specific API system calls for interoperability with external systems (rather than requiring a particular protocol or service to be integrated into a DCG 500).
  • Pipeline orchestrator 501 may ' spawn a plurality' of child pipeline clusters 502a-h, which may ' be used as dedicated workers for streamlining parallel processing. In some arrangements, an entire data processing pipeline may be passed to a child cluster 502a for handling, rather than individual processing tasks, enabling each child cluster 502a-b to handle an entire data pipeline in a dedicated fashion to maintain isolated processing of different pipelines using different cluster nodes 502a-b.
  • Pipeline orchestrator 501 may provide a software API for starting, stopping, submitting, or saving pipelines. When a pipeline is started, pipeline orchestrator 501 may send the pipeline information to an available worker node 502a-b, for example using AKKATM clustering.
  • a reporting object with status information may be maintained.
  • Streaming activities may report the last time an event was processed, and the number of events processed.
  • Batch activities may report status messages as they occur.
  • Pipeline orchestrator 501 may perform batch caching using, for example, an IGFSTM caching filesystem. This allows activities 512a-d within a pipeline 502a-b to pass data contexts to one another, with any necessary parameter configurations.
  • a pipeline manager 511a-b may be spawned for eveiy new running pipeline, and may be used to send activity, status, lifecycle, and event count information to the pipeline orchestrator 501.
  • a plurality of activity actors 512a-d may be created by a pipeline manager 511a-b to handle individual tasks, and provide output to data services 522a-d.
  • Data models used in a given pipeline may be determined by the specific pipeline and activities, as directed by a pipeline manager 511a-b.
  • Each pipeline manager 511a-b controls and directs the operation of any activity actors 512a-d spawned by it.
  • a pipeline process may need to coordinate streaming data between tasks.
  • a pipeline manager 511a ⁇ b may spawn service connectors to dynamically ' create TCP connections between activity' instances 512a-d.
  • Data contexts may be maintained for each individual activit 512a-d, and may ' be cached for provision to other activities 512a-d as needed,
  • a data context defines how an activity accesses information, and an activity 512a-d may' process data or simply forward it to a next step. Forwarding data between pipeline steps may ' route data through a streaming context or batch context.
  • a client service cluster 530 may operate a plurality' of service actors 521 a-d to serve the requests of activity ' actors 512a-d, ideally maintaining enough service actors 521a-d to support each activity' per the service type. These may' also be arranged within service clusters 520a-d, in a manner similar to the logical organization of activity actors 512a-d within clusters 502a-b in a data pipeline.
  • a logging service 530 may be used to log and sample DCG requests and messages during operation while notification service 540 may be used to receive alerts and other notifications during operation (for example to alert on errors, which may then be diagnosed by reviewing records from logging service 530), and by being connected externally to messaging system 510, logging and notification services can be added, removed, or modified during operation without impacting DCG 500.
  • a plurality of DCG protocols 55Qa-b may be used to provide structured messaging between a DCG 500 and messaging system 510, or to enable messaging system 510 to distribute DCG messages across service clusters 52Qa ⁇ d as shown.
  • a service protocol 560 may be used to define service interactions so that a DCG 500 may be modified without impacting service implementations. In this manner it can be appreciated that the overall structure of a system using an actor-driven DCG 500 operates in a modular fashion, enabling modification and substitution of various components without impacting other operations or requiring additional reconfiguration.
  • Fig. 7 is a diagram of an exemplary architecture for a system for rapid predictive analysis of very large data sets using an actor-driven distributed computational graph 500, according to one aspect.
  • a variant messaging arrangement may utilize messaging system 510 as a messaging broker using a streaming protocol 610, transmitting and receiving messages immediately using messaging system 510 as a message broker to bridge communication between service actors 521a.-b as needed.
  • individual services 522a-b may be utilized as a messaging broker using a streaming protocol 610, transmitting and receiving messages immediately using messaging system 510 as a message broker to bridge communication between service actors 521a.-b as needed.
  • individual services 522a-b may
  • Fig. 8 is a diagram of an exemplary architecture for a system for rapid predictive analysis of very ' large data sets using an actor-driven distributed computational graph 500, according to one aspect.
  • a variant messaging arrangement may utilize a service connector 710 as a central message broker between a pluralit of service actors 521a-b, bridging messages in a streaming context 610 while a data context service 630 continues to provide direct peer-to-peer messaging between individual services 522a-b in a batch context 620.
  • FIG. 9 is a diagram of an exemplary architecture 800 for a user and entity' behavioral analysis system, according to one aspect.
  • Architecture 800 may comprise a plurality of users 8Q5a-u, which may be individuals or connected devices, connecting to a user and entity behavioral analysis system 810.
  • System 810 may comprise a grouping engine 813, a behavioral analysis engine 819, a monitoring service 822, and a multidimensional time series data store 120 for storing gathered and processed data.
  • Grouping engine 813 may be configured to gather and identify user interactions and related metrics, which may include volume of interaction, frequency of interaction, and the like. Grouping engine 813 may use graph stack service 145 and DCG module 155 to convert: and analyze the data in graph format. The interaction data may ' then be used to split users 8G5a-n into a plurality of groups 816a-n Groupings may be based on department, project teams, interaction frequency, and other metrics which may be user-defined. Groupings may not be permanent, and may be adjusted and changed in real-time as group dynamics change. This may ' be automated by system 810, or an administrative user may' manually change the groupings.
  • Behavioral analysis engine 819 may batch process and aggregate overall usage logs, access logs, KERBEROS session data, SAME session sata, or data collected through the use of other network monitoring tools commonly used in the art such as BRO or 5URICATA. The aggregated data may' then be used to generate a behavioral baseline for each group established by grouping engine 813. Behavioral analysis engine 819 may use graph stack se dee 145 and DCG module 155 to convert and analyze the data in graph format using various machine learning models, and may also process the data using parallel computing to quickly process large amounts of data. Models may' be easily ' added to the system. Behavioral analysis engine 819 may' also be configured to process internal communications, such as email, using natural language processing.
  • Monitoring service 822 may actively' monitor groups for anomalous behavior, as based the established baseline. For example, monitoring service 822 may use the data pipelines of ACDP system 100 or multidimensional time series data store 120 to conduct real-time monitoring of various network resource sensors.
  • aspects that may ' be monitored may ' include, but is not limited to, anomalous web browsing, for example, the number of distinct domains visited exceeding a predefined threshold; anomalous data exfiltration, for example, the amount of outgoing data exceeding a predefined threshold; unusual domain access, for example, a subgroup consisting a few members within an established group demonstrating unusual browsing behavior by accessing an unusual domain a predetermined number of times within a certain timeframe; anomalous login times, for example, a user logging into a workstation during off- hours; unlikely ' login locations, for example, a user logging in using an account from two distinct locations that may ' be physically impossible within a certain timeframe; anomalous service access, for example, unusual application access or usage pattern; and new ' machines, for example, a user logging into a machine or server not typically accessed.
  • anomalous web browsing for example, the number of distinct domains visited exceeding a predefined threshold
  • anomalous data exfiltration for example, the amount of outgoing data exceeding a predefined threshold
  • Fig. 10 is a flow diagram of an exemplary method 1000 for cyberseeuriiy behavioral analytics, according to one aspect.
  • behavior analytics may utilize passive information feeds from a plurality of existing endpoints (for example, including but not limited to user activity on a network, network performance, or device behavior) to generate security solutions.
  • a web crawler 115 may ' passively ' collect activity ' information, which may then be processed 1002 using a DCG 155 to analyze behavior patterns.
  • anomalous behavior may' be recognized 1003 (for example, based on a threshold of variance from an established pattern or trend) such as high-risk users or malicious software operators such as bots.
  • anomalous behaviors may then be used 1004 to analyze potential angles of attack and then produce 1005 security suggestions based on this second-level analysis and predictions generated by' an action outcome simulation module 125 to determine the likely' effects of the change.
  • the suggested behaviors may then be automatically ' implemented 1006 as needed.
  • Passive monitoring 1001 then continues, collecting information after new security' solutions are implemented 1006, enabling machine learning to improve operation over time as the relationship between security changes and observed behaviors and threats are observed and analyzed.
  • This method 1000 for behavioral analytics enables proactive and high-speed reactive defense capabilities against a variety of cyberattack threats, including anomalous human behaviors as well as nonhuman“bad actors” such as automated software bots that may probe for, and then exploit, existing vulnerabilities.
  • Fig. 11 is a flow diagram of an exemplary method 1100 for measuring the effects of cybersecurity attacks, according to one aspect.
  • impact assessment of an attack may be measured using a DCG 155 to analyze a user account and identify its access capabilities 1101 (for example, what files, directories, devices or domains an account may have access to). This may then be used to generate 1102 an impact assessment score for the account, representing the potential risk should that account be compromised, in the event of an incident, the impact assessment score for any compromised accounts may be used to produce a“blast radius” calculation 1103, identifying exactlv what resources are at risk as a result of the intrusion and where security personnel should focus their attention.
  • simulated intrusions may be run 1104 to identify potential blast radius calculations for a variety of attacks and to determine 1105 high risk accounts or resources so that security may be improved in those key areas rather than focusing on reactive solutions.
  • Fig. 12 is a flow diagram of an exemplary method 1200 for continuous cybersecurity monitoring and exploration, according to one aspect.
  • a state observation service 140 may receive data from a variety of connected systems 1201 such as (for example, including but not limited to) servers, domains, databases, or user directories. This information may be received continuously, passively collecting events and monitoring activity over time while feeding 1202 collected information into a graphing service 145 for use in producing time-series graphs 1203 of states and changes over time. This collated time-series data may then be used to produce a visualization 1204 of changes over time, quantifying collected data into a meaningful and understandable format.
  • connected systems 1201 such as (for example, including but not limited to) servers, domains, databases, or user directories.
  • This information may be received continuously, passively collecting events and monitoring activity over time while feeding 1202 collected information into a graphing service 145 for use in producing time-series graphs 1203 of states and changes over time.
  • This collated time-series data may then be used to
  • Fig. 13 is a flow diagram of an exemplary' method 1300 for mapping a cyber-physical system graph (CPG), according to one aspect.
  • a cyber-physical system graph may comprise a visualization of hierarchies and relationships between devices and resources in a security infrastructure, contextualizing security information with physical device relationships that are easily understandable for security personnel and users.
  • behavior analytics information (as described previously ' , referring to Fig. 10 ⁇ may be received at a graphing service 145 for inclusion in a CPG.
  • impact assessment scores (as described previously', referring to Fig. 11) may be received and incorporated in the CPG information, adding risk assessment context to the behavior information.
  • time-series information (as described previously ' , referring to Fig. 12) may be received and incorporated, updating CPG information as changes occur and events are logged.
  • This information may then be used to produce 1304 a graph visualization of users, servers, devices, and other resources correlating physical relationships (such as a user’s personal computer or smartphone, or physical connections between servers) with logical relationships (such as access privileges or database connections), to produce a meaningful and contextualized visualization of a security infrastructure that reflects the current state of the internal relationships present in the infrastructure.
  • Fig. 14 is a flow diagram of an exemplary method 1400 for continuous network resilience scoring, according to one aspect.
  • a baseline score can be used to measure an overall level of risk for a network infrastructure, and may' be compiled by first collecting 1401 information on publicly-disclosed vulnerabilities, such as (for example) using the Internet or common vulnerabilities and exploits (CVE) process.
  • CVE common vulnerabilities and exploits
  • This information may then 1402 be incorporated into a CPG as described previously' in Fig. 13, and the combined data of the CPG and die known vulnerabilities may then be analyzed 1403 to identify' die relationships between known vulnerabilities and risks exposed by components of die infrastructure.
  • This produces a combined CPG 1404 that incorporates both the internal risk level of network resources, user accounts, and devices as well as the actual risk level based on the analysis of known vulnerabilities nil security risks.
  • Fig. 15 is a flow diagram of an exemplary' method 1500 for cybersecurity privilege oversight, according to one aspect.
  • time-series data (as described above, referring to Fig. 12) may be collected 1501 for user accounts, credentials, directories, and other user-based privilege and access information. This date may then 1502 be analyzed to identify changes over time that may affect security', such as modifying user access privileges or adding new users. The results of analysis may' be checked 1503 against a CFG (as described previously in Fig. 13), to compare and correlate user directory changes with the actual infrastructure state.
  • CFG as described previously in Fig. 13
  • This comparison may be used to perform accurate and context-enhanced user directory audits 1504 that identify not only current user credentials and other user-specific information, but changes to this information over time and how the user information relates to the actual infrastructure (for example, credentials that grant access to devices and may therefore implicitly grant additional access due to device relationships that were not immediately apparent from the user directory alone).
  • Fig. 16 is a flow diagram of an exemplary' method 1600 for cybersecurity risk management, according to one aspect.
  • multiple methods described previously may ' be combined to provide live assessment of attacks as they occur, by first receiving 1601 time-series data for an infrastructure (as described previously ' , in Fig. 12) to provide live monitoring of network events.
  • This data is then enhanced 1602 with a CPG (as described above in Fig. 13) to correlate events with actual infrastructure elements, such as servers or accounts.
  • an event for example, an attempted atack against a vulnerable system or resource
  • the event is logged in the time-series data 1604, and compared against the CPG 1605 to determine the impact.
  • This is enhanced with the inclusion of impact assessment information 1606 for any affected resources, and the attack is then checked against a baseline score 1607 to determine the full extent of the impact of the attack and any necessary
  • Fig. 17 is a flow ' diagram of an exemplary' method 1700 for mitigating compromised credential threats, according to one aspect.
  • impact assessment scores (as described previously', referring to Fig. 11) may be collected 1701 for user accounts in a director ⁇ , so dial the potential impact of any given credential attack is known in advance of an actual attack event.
  • This information may be combined with a CFG 1702 as described previously in Fig. 13, to contextualize impact assessment scores within the infrastructure (for example, so that it may be predicted what systems or resources might be at risk for any given credential attack).
  • a simulated attack may then be performed 1703 to use machine learning to improve security without waiting for actual attacks to trigger a reactive response.
  • a blast radius assessment (as described above in
  • Fig. 11 may be used in response 1704 to determine the effects of the simulated attack and identify points of -weakness, and produce a recommendation report 1705 for improving and hardenin og the infrastructure ag oainst future attacks.
  • Fig. 18 is a flow diagram of an exemplary method 1800 for dynamic network and rogue device discovery, according to one aspect.
  • an advanced cyber decision platform may continuously monitor a network in real-time 1801, detecting any changes as they occur.
  • a CPG may be updated 1803 with the new connection information, which may ' then be compared against the network’s resiliency score 1804 to examine for potential risk.
  • the blast radius metric for any other devices involved in the connection may also be checked 1805, to examine the context of the connection for risk potential (for example, an unknown connection to an internal data server with sensitive information may ' be considered a much higher risk than an unknown connection to an externally-facing web server). If the connection is a risk, an alert may' be sent to an administrator 1806 with the contextual information for the connection to provide a concise notification of relevant details for quick handling.
  • Fig. 19 is a flow diagram of an exemplary method 1900 for Kerberos“golden ticket” attack and“golden SAML” attack detection, according to one aspect.
  • behavioral analytics may be employed to detect erroneously-issued authentication tickets or forged AO’s, whether from incorrect configuration or from an attack.
  • an advanced cyber decision platform may continuously' monitor a network 1901, informing a CPG in real-time of all traffic associated with people, places, devices, or services 1902.
  • Machine learning algorithms detect behavioral anomalies as they occur in real-time 1903, notifying administrators with an assessment of the anomalous event 1904 as well as a blast radius score for the particular event and a network resiliency score to advise of the overall health of the network.
  • a compromised ticket is immediately detected when a new authentication connection is made.
  • Fig. 20 is a flow diagram of an exemplary method 2000 for risk-based vulnerability and patch management, according to one aspect.
  • an advanced cyber decision platform may monitor all information about a network 2001, including (but not limited to) device telemetry data, log files, connections and network events, deployed software versions, or contextual user activity information. This information is incorporated into a CFG 2002 to maintain an up-to-date model of the network in real-time.
  • a blast radius score may be assessed 2003 and the network’s resiliency score may be updated 2004 as needed.
  • a security alert may then be produced 2005 to notify an administrator of the vulnerability and its impact, and a proposed patch may be presented 2006 along with die predicted effects of the patch on the vulner ability’s blast radius and the overall network resiliency score. This determines both the total impact risk of any particular vulnerability, as well as the overall effect of each vulnerability on the network as a whole.
  • This continuous network assessment may be used to collect information about ne ' vulnerabilities and exploits to provide proactive solutions with clear result predictions, before attacks occur.
  • Fig. 21 is a flow diagram of an exemplary method 2100 for establishing groups of users according to one aspect.
  • data pertaining to network interaction between users and devices are gathered by a grouping engine.
  • the grouping engine may ' dien process the gathered information by converting it to a graph format and using DCG module to establish groupings for users.
  • a system administrator may provide additional input, and fine- tune the groupings if required.
  • a behavioral baseline is established for each group that may ' be based on the interaction information, network logs, connected devices, and the like.
  • groups are continuous monitored for anomalous behavior.
  • Fig. 22 is a flow diagram of an exemplary method 2200 for monitoring groups for anomalous behavior, according to one aspect.
  • a system as described above in Fig. 8, gathers network-related data.
  • This data may comprise usage logs, Kerberos sessions data, SAML sessions data, computers and other devices connected to the network, active users, software installed, and the like.
  • a behavioral analysis engine may process the data. Parallel computing may be used to speed up the processing of the data. The data may then be soiled by, and associated to, previously established groupings.
  • a behavioral baseline score is generated for each group based on the results of die data processing.
  • the data is stored into a time-series graph database.
  • the process repeats periodically to create snapshots of various moments in time, and stored into the database. This may allow the system to retrain the baseline to take into considering non-anomalous baseline variances that may occur over time, as well as forecast changes in group dynamics using predictive analysis functions of ACDP system 100.
  • Fig. 23 is a flow diagram for an exemplary' method 2300 for handing a detection of anomalous behavior, according to one aspect.
  • the system detects anomalous user behavior from a group. This may ' be based on comparison to established baselines, or a high priority incident caught during routine monitoring, for example a device accessing a blacklisted domain.
  • the system investigates the group in which the anomalous behavior originated. This may include a more thorough analysis of usage and access logs. If applicable, users or devices with higher access privileges may ' be investigated before those with lower access privileges.
  • the source or sources of the anomalous behavior is identified, and some corrective measures may be taken.
  • the offending device or user account may be automatically locked out of tire network until a solution has been implemented.
  • group members and system admini trators may be notified.
  • the system may utilize the various techniques discussed above to recommend a corrective action, or the system may take action automatically.
  • Fig. 24 is a flow diagram illustrating an exemplary method 2400 for processing a new user connection, according to one aspect.
  • system 910 detects a user connecting to a monitored service provider.
  • step 2406 if the user is connecting with an existing AO, the process leads to the method discussed in Fig. 25 at step 2409
  • Fig. 25 is a flow diagram illustrating an exemplary method 2500 for verifying the authenticity of an authentication object, according to one aspect.
  • a user with an AO connects to a monitored service provider.
  • system 910 detects the connection request, retrieves the AO, and generates a cryptographic hash for the AO. System 910 may now compare the newly generated hashes with previous generated hashes stored in memory.
  • the connect proceeds as normal and method 2500 ends at step 2512 as no further action for this session is required.
  • the AO is determined to be forged, method 2500 goes to step 2515 where EGA rales may be triggered to perform their preset functions, and perform“circuit breaker” checks within a user-configurable time period.
  • a network administrator at step may be notified, and sent any relevant information, such as blast radius, access logs for the forged AO connection, and the like.
  • the techniques disclosed herein may be implemented on hardware or a combination of software and hardware. For example, they may be implemented in an operating system kernel, in a separate user process, in a library package bound into network applications, on a specially constructed machine, on an application-specific integrated circuit (ASIC), or on a network interface card.
  • ASIC application-specific integrated circuit
  • Software/hardware hybrid implementations of at least some of the aspects disclosed herein may be implemented on a programmable network-resident machine (which should be understood to include intermittently connected network-aware machines) selectively activated or reconfigured by a computer program stored in memory.
  • Such network devices may have multiple network interfaces that may be configured or designed to utilize different types of network communication protocols.
  • a general architecture for some of these machines may be described herein in order to illustrate one or more exemplary means by which a given unit of functionality may be implemented.
  • At least some of the features or functionalities of the various aspects disclosed herein may be implemented on one or more general-purpose computers associated with one or more networks, such as for example an end- user computer system, a client computer, a network server or other server system, a mobile computing device (e.g., tablet computing device, mobile phone, smartphone, laptop, or other appropriate computing device), a consumer electronic device, a music player, or any other suitable electronic device, router, switch, or other suitable device, or any combination thereof.
  • at least some of the features or functionalities of the various aspects disclosed herein may be implemented in one or more virtualized computing environments (e.g., network computing clouds, virtual machines hosted on one or more physical computing machines, or other appropriate virtual environments).
  • FIG. 26 there is shown a block diagram depicting an exemplary computing device 10 suitable for implementing at least a portion of the features or functionalities disclosed herein.
  • Computing device 10 may be, for example, any one of the computing machines listed in the previous paragraph, or indeed any other electronic device capable of executing softw re- or hardware-based instructions according to one or more programs stored in memory.
  • Computing device 10 may be configured to communicate with a plurality of other computing devices, such as clients or servers, over communications networks such as a wide area network a metropolitan area network, a local area network, a wireless network, the Internet, or any other network, using known protocols for such communication, whether wireless or wired.
  • communications networks such as a wide area network a metropolitan area network, a local area network, a wireless network, the Internet, or any other network, using known protocols for such communication, whether wireless or wired.
  • computing device 10 includes one or more central processing units (CPU) 12, one or more interfaces 15, and one or more busses 14 (such as a peripheral component interconnect (PCI) bus).
  • CPU central processing units
  • interfaces such as a USB interface
  • busses 14 such as a peripheral component interconnect (PCI) bus
  • CPU 12 may be responsible for implementing specific functions associated with the functions of a specifically configured computing device or machine.
  • a computing device 10 may be configured or designed to function as a server system utilizing CPU 12, local memory' 11 and/or remote memory 16, and interface (s) 15.
  • CPU 12 may be caused to perform one or more of the different types of functions and/or operations under the control of software modules or components, which for example, may include an operating system and any appropriate applications software, drivers, and the like,
  • CPU 12 may include one or more processors 13 such as, for example, a processor from one of the Intel, ARM, Qualcomm, and AMD families of microprocessors.
  • processors 13 may include specially designed hardware such as application-specific integrated circuits (ASICs), electrically erasable programmable read-only memories (EEPROMs), field- programmable gate arrays (FPGAs), and so forth, for controlling operations of computing device 10.
  • ASICs application-specific integrated circuits
  • EEPROMs electrically erasable programmable read-only memories
  • FPGAs field- programmable gate arrays
  • a local memory 11 such as non-volatile random access memory (RAM) and/or read-only memor - (ROM), including for example one or more levels of cached memory
  • RAM non-volatile random access memory
  • ROM read-only memor -
  • Memory' 11 may be used for a variety of purposes such as, for example, caching and/or storing data, programming instructions, and the like. It should be further appreciated that CPU 12 may be one of a variety of system-on-a-chip (SOC) type hardware that may include additional hardware such as memory or graphics processing chips, such as a QUALCOMM SNAPDRAGONTM or SAMSUNG EXYNOSTM CPU as are becoming increasingly common in the art, such as for use in mobile devices or integrated devices.
  • SOC system-on-a-chip
  • processor is not limited merely to those integrated circuits referred to in the art as a processor, a mobile processor, or a microprocessor, but broadly refers to a microcontroller, a microcomputer, a programmable logic controller, an application-specific integrated circuit, and any other programmable circuit.
  • interfaces 15 are provided as network interface cards (NICs), Generally, NICs control the sending and receiving of data packets over a computer network; other types of interfaces 15 may for example support other peripherals used with computing device 10.
  • NICs network interface cards
  • NICs control the sending and receiving of data packets over a computer network
  • other types of interfaces 15 may for example support other peripherals used with computing device 10.
  • the interfaces that may be provided are Ethernet interfaces, frame relay interfaces, cable interfaces, DSL interfaces, token ring interfaces, graphics interfaces, and the like.
  • interfaces may be provided such as, for example, universal serial bus (USB), Serial, Ethernet, FIREWIRETM, THUNDERBOLTTM, PCI, parallel, radio frequency (RF), BLUETOOTHTM, near-field communications (e.g., using near-field magnetics), 802.11 (WiFi), frame relay, TCP/IP, ISDN, fast Ethernet interfaces, Gigabit Ethernet interfaces, Serial ATA (SATA) or external SATA (ESATA) interfaces, high-definition multimedia interface (HDMI), digital visual interface (DVI), analog or digital audio interfaces, asynchronous transfer mode (ATM) interfaces, high-speed serial interface (HSSI) interfaces, Point of Sale (POS) interfaces, fiber data distributed interfaces (FDDIs), and the like.
  • USB universal serial bus
  • RF radio frequency
  • BLUETOOTHTM near-field communications
  • near-field communications e.g., using near-field magnetics
  • WiFi wireless FIREWIRETM
  • Such interfaces 15 may include physical ports appropriate for communication with appropriate media. In some cases, they may also include an independent processor (such as a dedicated audio or video processor, as is common in the art for high-fidelity A/V hardware interfaces) and, in some instances, volatile and/or non-volatile memory (e.g., RAM).
  • an independent processor such as a dedicated audio or video processor, as is common in the art for high-fidelity A/V hardware interfaces
  • volatile and/or non-volatile memory e.g., RAM
  • a single processor 13 handles communications as well as routing computations, while in other aspects a separate dedicated communications processor may ⁇ be provided.
  • different types of features or functionalities may ' be implemented in a system according to the aspect that includes a client device (such as a tablet device or smartphone running client software) and server systems (such as a server system described in more detail below).
  • the system of an aspect may employ one or more memories or memory- modules (such as, for example, remote memory block 16 and local memory' 11) configured to store data, program instructions for the general-purpose network operations, or other information relating to the functionality of the aspects described herein (or any- combinations of the above).
  • Program instructions may control execution of or comprise an operating system and/or one or more applications, for example.
  • Memory- 16 or memories 11, 16 may- also be configured to store data structures, configuration data, encryption data, historical system operations information, or any other specific or generic non-program information described herein.
  • At least some network device aspects may- include nonfransitory machine-readable storage media, which, for example, may- be configured or designed to store program instructions, state information, and the like for performing various operations described herein.
  • nontransitory machine-readable storage media include, but are not limited to, magnetic media such as hard disks, floppy disks, and magnetic tape; optical media such as CD-ROM disks; magneto-optical media such as optical disks, and hardware devices that are specially configured to store and perform program instructions, such as read-only memory- devices (ROM), flash memory- (as is common in mobile devices and integrated systems), solid state drives (SSD) and“hybrid SSD” storage drives that may- combine physical components of solid state and hard disk drives in a single hardware device (as are becoming increasingly common in the art with regard to personal computers), memristor memory, random access memory (RAM), and the like.
  • ROM read-only memory- devices
  • flash memory- as is common in mobile devices and integrated systems
  • SSD solid state drives
  • HDD hard disk drives
  • RAM random access memory
  • such storage means may be integral and non-removable (such as RAM hardware modules that may be soldered onto a motherboard or otherwise integrated into an electronic device), or they may be removable such as swappable flash memory ' modules (such as“thumb drives” or other removable media designed for rapidly exchanging physical storage devices),“hot-swappable” hard disk drives or solid state drives, removable optical storage discs, or other such removable media, and that such integral and removable storage media may be utilized interchangeably.
  • program instructions include both object code, such as may be produced b a compiler, machine code, such as may be produced by an assembler or a linker, byte code, such as may be generated by for example a JAVATM compiler and may be executed using a Java virtual machine or equivalent, or files containing higher level code that may be executed by the computer using an interpreter (for example, scripts written in Python, Perl, Ruby, Groovy, or any other scripting language).
  • interpreter for example, scripts written in Python, Perl, Ruby, Groovy, or any other scripting language.
  • systems may' be implemented on a standalone computing system.
  • Computing device 20 includes processors 21 that may run software that carry out one or more functions or applications of aspects, such as for example a client application 24, Processors 21 may carry out computing instructions under control of an operating system 22 such as, for example, a version of MICROSOFT WINDOWSTM operating system, APPLE macOSTM or iOSTM operating systems, some variety ' of the Linux operating system, ANDROIDTM operating system, or the like.
  • an operating system 22 such as, for example, a version of MICROSOFT WINDOWSTM operating system, APPLE macOSTM or iOSTM operating systems, some variety ' of the Linux operating system, ANDROIDTM operating system, or the like.
  • one or more shared services 23 may be operable in system 20, and may be useful for providing common services to client applications 24.
  • Services 23 may for example be WINDOWSTM services, user-space common services in a Linux environment, or any other type of common service architecture used with operating system 21.
  • Input devices 28 maybe of any type suitable for receiving user input, including for example a keyboard, touchscreen, microphone (for example, for voice input), mouse, touchpad, trackball, or any combination thereof.
  • Output devices 27 may be of any type suitable for providing output to one or more users, whether remote or local to system 20, and may include for example one or more screens for visual output, speakers, printers, or any combination thereof.
  • Memor ⁇ 25 may- be random- access memory having any structure and architecture known in the art, for use by processors 21, for example to run software.
  • Storage devices 26 may be any magnetic, optical, mechanical, memristor, or electrical storage device for storage of data in digital form (such as those described above, referring to Fig, 26). Examples of storage devices 26 include flash memory, magnetic hard drive, CD-ROM, and/or the like,
  • systems may be implemented on a distributed computing network, such as one having any number of clients and/or servers.
  • Fig. 28 there is shown a block diagram depicting an exemplary architecture 30 for implementing at least a portion of a system according to one aspect on a distributed computing network.
  • any number of clients 33 may be provided.
  • Each client 33 may run software for implementing client- side portions of a system; clients may comprise a system 20 such as that illustrated in Fig. 27,
  • any number of servers 32 may be provided for handling requests received from one or more clients 33.
  • Clients 33 and servers 32 may communicate with one another via one or more electronic networks 31, which may be in various aspects any of the Internet, a wide area network, a mobile telephony network (such as CDMA or GSM cellular networks), a wireless network (such as WiFi, WiMAX, LTE, and so forth), or a local area network (or indeed any network topology known in the art; the aspect does not prefer any one network topology over any other).
  • Networks 31 may be implemented using any known network protocols, including for example wared and/or wireless protocols.
  • servers 32 may call external services 37 when needed to obtain additional information, or to refer to additional data concerning a particular call.
  • external services 37 may take place, for example, via one or more networks 31.
  • external services 37 ay ' comprise web-enabled services or functionality related to or installed on the hardware device itself.
  • client applications 24 may obtain information stored in a server system 32 in the cloud or on an external service 37 deployed on one or more of a particular enterprise’s or user’s premises.
  • clients 33 or servers 32 may snake use of one or more specialized services or appliances that may ' be deployed locally or remotely across one or more networks 31.
  • one or more databases 34 may' be used or referred to by one or more aspects. It should be understood by one having ordinary' skill in the art that databases 34 may be arranged in a wide variety of architectures and using a wide variety of data access and manipulation means.
  • one or more databases 34 may comprise a relational database system using a structured query ' language (SQL), while others may comprise an alternative data storage technology such as those referred to in the art as“NoSQL” (for example, HADOOP CASSANDRATM, GOOGLE BIGTABLETM, and so forth).
  • SQL structured query ' language
  • variant database architectures such as column-oriented databases, in-memorv databases, clustered databases, distributed databases, or even flat file data repositories may be used according to the aspect.
  • any ' combination of knowni or future database technologies may' be used as appropriate, unless a specific database technology' or a specific arrangement of components is specified for a particular aspect described herein.
  • the term“database” as used herein may' refer to a physical database machine, a cluster of machines acting as a single database system, or a logical database within an overall database management system.
  • security ' systems 36 and configuration systems 35 may make use of one or more security ' systems 36 and configuration systems 35, Security' and configuration management are common information technology (IT) and web functions, and some amount of each are generally associated with any IT or web systems. It should be understood by one having ordinary skill in the art tlrat any' configuration or security subsystems known in the art now' or in the future may ' be used in conjunction with aspects without limitation, unless a specific security ' 36 or configuration system 35 or approach is specifically required by the description of any specific aspect.
  • IT information technology
  • Fig. 29 shows an exemplary' overview of a computer system 40 as may be used in any of the various locations throughout the system. It is exemplary of any' computer that may ' execute code to process data. Various modifications and changes may ' be made to computer system 40 without departing from the broader scope of the system and method disclosed herein.
  • Central processor unit (CPU) 41 is connected to bus 42, to which bus is also connected memory' 43, nonvolatile memory 44, display' 47, input/output (I/O) unit 48, and network interface card (NIC) 53.
  • I/O unit 48 may', typically', be connected to keyboard 49, pointing device 50, hard disk 52, and real-time clock 51.
  • NIC 53 connects to network 54, which may be the Internet or a local network, which local network may or may not have connections to die Internet.
  • power supply unit 45 connected, in this example, to a main alternating current (AC) supply 46.
  • AC alternating current
  • functionality for implementing systems or methods of various aspects may ' be distributed among any number of client and/or server components.
  • various software modules may ' be implemented for performing various functions in connection with the system of any particular aspect, and such modules may be variously implemented to run on server and/or client components.

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Abstract

L'invention concerne un système permettant de détecter et d'atténuer des attaques par objet d'authentification falsifié, qui comprend : un dispositif d'inspection d'objet d'authentification configuré pour étudier un nouvel objet d'authentification généré par un fournisseur d'identité et récupérer ce nouvel objet d'authentification; et un moteur de hachage configuré pour récupérer le nouvel objet d'authentification auprès du dispositif d'inspection d'objet d'authentification, calculer un hachage cryptographique pour le nouvel objet d'authentification, et mémoriser le hachage cryptographique du nouvel objet d'authentification dans une mémoire de données, des demandes d'accès ultérieures accompagnées d'objets d'authentification étant validées par comparaison du hachage de chaque objet d'authentification à des hachages générés précédents.
PCT/US2018/064541 2017-12-07 2018-12-07 Détection et atténuation d'attaques par objet d'authentification falsifié au moyen d'une plateforme de cyberdécision avancée WO2019113492A1 (fr)

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EP18885538.1A EP3721364A4 (fr) 2017-12-07 2018-12-07 Détection et atténuation d'attaques par objet d'authentification falsifié au moyen d'une plateforme de cyberdécision avancée
CN201880076822.4A CN111492360A (zh) 2017-12-07 2018-12-07 使用先进网络决策平台检测并减缓伪造认证对象攻击

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US201762596105P 2017-12-07 2017-12-07
US62/596,105 2017-12-07
US15/837,845 2017-12-11
US15/837,845 US11005824B2 (en) 2015-10-28 2017-12-11 Detecting and mitigating forged authentication object attacks using an advanced cyber decision platform

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Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20030145225A1 (en) * 2002-01-28 2003-07-31 International Business Machines Corporation Intrusion event filtering and generic attack signatures
US20110087888A1 (en) * 2009-10-13 2011-04-14 Google Inc. Authentication using a weak hash of user credentials
US20160072845A1 (en) * 2003-12-30 2016-03-10 Entrust, Inc. Method and apparatus for providing authentication using policy-controlled authentication articles and techniques

Family Cites Families (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US9729538B2 (en) * 2014-09-01 2017-08-08 Microsoft Israel Research And Development (2002) Ltd System, method and process for detecting advanced and targeted attacks with the recoupling of kerberos authentication and authorization

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20030145225A1 (en) * 2002-01-28 2003-07-31 International Business Machines Corporation Intrusion event filtering and generic attack signatures
US20160072845A1 (en) * 2003-12-30 2016-03-10 Entrust, Inc. Method and apparatus for providing authentication using policy-controlled authentication articles and techniques
US20110087888A1 (en) * 2009-10-13 2011-04-14 Google Inc. Authentication using a weak hash of user credentials

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
See also references of EP3721364A4 *

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