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
  • G06F21/50—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
  • G06F21/552—Detecting local intrusion or implementing counter-measures involving long-term monitoring or reporting
• • 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
  • G06F21/57—Certifying or maintaining trusted computer platforms, e.g. secure boots or power-downs, version controls, system software checks, secure updates or assessing vulnerabilities
  • G06F21/577—Assessing vulnerabilities and evaluating computer system security
• • G—PHYSICS
  • G06—COMPUTING; CALCULATING OR COUNTING
  • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
  • G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
  • G06Q40/02—Banking, e.g. interest calculation or account maintenance
• • H—ELECTRICITY
  • H04—ELECTRIC COMMUNICATION TECHNIQUE
  • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
  • H04L63/00—Network architectures or network communication protocols for network security
  • H04L63/14—Network architectures or network communication protocols for network security for detecting or protecting against malicious traffic
  • H04L63/1408—Network architectures or network communication protocols for network security for detecting or protecting against malicious traffic by monitoring network traffic
  • H04L63/1416—Event detection, e.g. attack signature detection
• • H—ELECTRICITY
  • H04—ELECTRIC COMMUNICATION TECHNIQUE
  • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
  • H04L63/00—Network architectures or network communication protocols for network security
  • H04L63/14—Network architectures or network communication protocols for network security for detecting or protecting against malicious traffic
  • H04L63/1433—Vulnerability analysis

Definitions

• the disclosure relates to the field of computer management, and more particularly to the field of cybersecurity and threat analytics.
• PALANTIRTM offers software to isolate patterns in large volumes of data
• DATABRICKSTM offers custom analytics services
• ANAPLANTM offers financial impact calculation services.
• the inventor has developed a system for advanced cybersecurity threat mitigation for inter-bank financial transactions.
• a system for detection and mitigation of cyberattacks on interbank financial transactions sent through the SWIFT (Society for Worldwide Interbank Financial Telecommunications) network comprising: an interface with the SWIFT system, connected to an advanced cyber decision platform: a time series data store comprising at last a processor, a memory, and a plurality of programming instructions stored in the memory and operating on the processor, wherein upon operating the software instructions the processor is configured to monitor a plurality of network events and produce time-series data, the time-series data comprising at least a record of a network event and the time at which the event occurred; an action outcome simulation module comprising at last a processor, a memory, and a plurality of programming instructions stored in the memory and operating on the processor, wherein upon operating the software instructions the processor is configured to generate simulated network events, and configured to produce recommendations based at least in part on the results of analysis performed by the directed computational graph module; an observation and state estimation module comprising at last a processor, a memory, and a pluralit
• a method for mitigation of cyberattacks on inter-bank financial transactions employing an advanced cyber decision platform comprising the steps of: a) producing, using an observation and state estimation module, a cyber-physical graph representing a plurality of connected resources on a network; b) analyzing, using a directed computational graph module, at least a portion of the cyber-physical graph; c) simulating, using an action outcome simulation module, a plurality of network events; d) monitoring, using a time series data store, at least a portion of the network events; e) producing time-series data based at least in part on the network events; f) analyzing at least a portion of the time-series data; and g) producing a security recommendation based at least in part on the results of the analysis, is disclosed.
• FIG. 1 is a diagram of an exemplary architecture of an advanced cyber decision platform according to one aspect.
• Fig. 2 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. 2A is a flow diagram of an exemplary function of the business operating system for the detection and mitigation of cyberattacks on inter-bank financial transactions sent through the SWIFT network.
• Fig. 3 is a process diagram showing business operating system functions in use to mitigate cyberattacks.
• Fig. 4 is a process flow diagram of a method for segmenting cyberattack information to appropriate corporation parties.
• Fig. 5 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 aspect.
• 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, 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 aspect.
• Fig. 8 is a flow diagram of an exemplary method for cybersecurity behavioral analytics, according to one aspect.
• FIG. 9 is a flow diagram of an exemplary method for measuring the effects of
• cybersecurity attacks according to one aspect.
• Fig. 10 is a flow diagram of an exemplary method for continuous cybersecurity monitoring and exploration, according to one aspect.
• Fig. 11 is a flow diagram of an exemplary method for mapping a cyber-physical system graph, according to one aspect.
• Fig. 12 is a flow diagram of an exemplary method for continuous network resilience scoring, according to one aspect.
• Fig. 13 is a flow diagram of an exemplary method for cybersecurity privilege oversight, according to one aspect.
• Fig. 14 is a flow diagram of an exemplary method for cybersecurity risk management, according to one aspect.
• FIG. 15 is a flow diagram of an exemplary method for mitigating compromised credential threats, according to one aspect.
• Fig. 16 is a block diagram illustrating an exemplary hardware architecture of a computing device.
• Fig. 17 is a block diagram illustrating an exemplary logical architecture for a client device.
• Fig. 18 is a block diagram illustrating an exemplary architectural arrangement of clients, servers, and external services.
• Fig. 19 is another block diagram illustrating an exemplary hardware architecture of a computing device.
• the inventor has conceived, and reduced to practice, an advanced cybersecurity threat mitigation for inter-bank financial transactions.
• 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 the graph.
• Nodes can be further qualified by the connection of one or more descriptors or "properties” to that node. For example, given the node 'James 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”. Thus, given a second node “Thomas G,” an edge between 'James R” and “Thomas G” that indicates that the two people know each other might be labeled "knows.”
• 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 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.
• 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.
• 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 typically 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. While various aspects may preferentially employ one or another of the various data storage subsystems available in the art (or available in the future), the invention should not be construed to be so limited, as any data storage architecture may be used according to the aspects.
• 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 sv 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.
• source data context this may be a streaming context if the upstream activities are streaming
• 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. 1 is a diagram of an exemplary architecture of an advanced cyber decision platform (ACDP) 100 according to one aspect.
• CASSANDRATM or REDISTM CASSANDRATM or REDISTM 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 part of analysis or the decomposable transformer service 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 the 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 SCRAPYTM 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 time series data store module 120 may receive streaming data from a large plurality of sensors that 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 service 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 web crawling module 115 may be further analyzed and transformed into task optimized results by the directed
• computational graph 155 and associated general transformer service 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 pre-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 the already available data in the 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.
• the advanced cyber decision platform a specifically programmed usage of the business operating system, continuously monitors a client enterprise's normal network activity for behaviors such as but not limited to normal users on the 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 the 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-the-hash attack and the related ACTIVE DIRECTORYTM / Kerberos overpass-the-hash attack , ACTIVE DIRECTORYTM / Kerberos Skeleton Key, ACTIVE DIRECTORYTM / Kerberos golden and silver ticket attack, privilege escalation attack, compromised user credentials, and ransomware disk attacks.
• known cyberattack vectors such as, but not limited to, ACTIVE DIRECTORYTM / Kerberos pass-the- ticket attack, ACTIVE DIRECTORYTM / Kerberos pass-the-hash attack and the related ACTIVE DIRECTORYTM / Kerberos overpass-the-
• 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. 2 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 analyzed 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-site 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-site 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-site 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-
• 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 the individual usage profile 201.
• anomalous activities may include a user attempting to gain access several workstations or servers in rapid succession, or a user attempting to gain access to a domain server of server with sensitive information using random userlDs or another user's userlD and password, or attempts by any user to brute force crack a privileged user's password, or replay of recently issued ACTIVE DIRECTOR YTM/Kerberos ticket granting tickets, or the presence on any known, ongoing exploit on the network or the introduction of known malware to the network, just to name a very small sample of the cyberattack profiles known to those skilled in the field.
• 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 attack 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 attack 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. [051] Fig.
• the system 221 consists of a SWIFT terminal 222 for entering transactions, which is connected to the system interface 223. All transactions entered are passed to a transaction validator 224, which gathers system-wide data from a variety of sources, which may include network behavior 225, user behavior 226, and device behavior 227.
• baseline profiles 202 for inter-bank financial transactions are based on a sophisticated analysis of the normal usage patterns of network nodes related to inter-bank financial transactions such as transaction locations, endpoint identities, transaction details, transaction volumes, transaction amounts, and transaction timing, to name some of the variables which can be analyzed.
• SWIFT transactions passing through the system are compared in a multi-modal analysis 228 against the baseline profiles to detect anomalies, which could indicate suspicious activity.
• the transaction will be sent to a transaction router 229, which routes the transaction appropriately, depending on whether or not an anomaly was detected 230. If no anomaly is detected, the transaction will be processed as usual 235.
• the transaction will be placed on hold and routed for human intervention 231 to a plurality of user terminals 232 for human evaluation 233. If the human analysis determines that the transaction is legitimate, the transaction will be completed 235. Otherwise, the transaction will be denied 234, thus protecting the financial institutions involved from loss.
• Fig. 3 is a process diagram 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 362, to record compliance to standardized guidelines or SLA requirements 363, to continuously probe existing network infrastructure and issue alerts to any 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 the invention.
• Fig. 4 is a process flow diagram of a method for segmenting cyberattack information to appropriate corporation parties 400.
• one of the strengths of the advanced cyber-decision 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 the 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 the 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 was 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. 5 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-b, 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 every 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 activity 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 550a-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 520a-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. 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 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
• a batch context 620 communicates directly in a batch context 620, using a data context service 630 as a broker to batch-process and relay messages between services 522a-b.
• 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 a service connector 710 as a central message broker between a plurality 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. 8 is a flow diagram of an exemplary method 800 for cybersecurity 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, SWIFT transactions, or device behavior) to generate security solutions.
• a web crawler 115 may passively collect activity information, which may then be processed 802 using a DCG 155 to analyze behavior patterns.
• anomalous behavior may be recognized 803 (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 804 to analyze potential angles of attack and then produce 805 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 806 as needed.
• Passive monitoring 801 then continues, collecting information after new security solutions are implemented 806, 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 800 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.
• Using automated behavioral learning in this manner provides a much more responsive solution than manual intervention, enabling rapid response to threats to mitigate any potential impact.
• Utilizing machine learning behavior further enhances this approach, providing additional proactive behavior that is not possible in simple automated approaches that merely react to threats as they occur.
• Fig. 9 is a flow diagram of an exemplary method 900 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 901 (for example, what files, directories, devices or domains an account may have access to). This may then be used to generate 902 an impact assessment score for the account, representing the potential risk should that account be compromised.
• the impact assessment score for any compromised accounts may be used to produce a "blast radius" calculation 903, identifying exactly what resources are at risk as a result of the intrusion and where security personnel should focus their attention.
• Fig. 10 is a flow diagram of an exemplary method 1000 for continuous cybersecurity monitoring and exploration, according to one aspect.
• a state observation service 140 may receive data from a variety of connected systems 1001 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 1002 collected information into a graphing service 145 for use in producing time-series graphs 1003 of states and changes over time.
• This collated time-series data may then be used to produce a visualization 1004 of changes over time, quantifying collected data into a meaningful and understandable format. As new events are recorded, such as changing user roles or permissions, modifying servers or data structures, or other changes within a security
• Fig. 11 is a flow diagram of an exemplary method 1100 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. 8) may be received at a graphing service 145 for inclusion in a CPG.
• a next step 1102 impact assessment scores (as described previously, referring to Fig. 9) 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. 10) may be received and incorporated, updating CPG information as changes occur and events are logged. This information may then be used to produce 1104 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. [066] Fig.
• a baseline score can be used to measure an overall level of risk for a network infrastructure, and may be compiled by first collecting 1201 information on publicly-disclosed vulnerabilities, such as (for example) using the Internet or common vulnerabilities and exploits (CVE) process. This information may then 1202 be incorporated into a CPG as described previously in Fig. 11, and the combined data of the CPG and the known vulnerabilities may then be analyzed 1203 to identify the relationships between known vulnerabilities and risks exposed by components of the infrastructure. This produces a combined CPG 1204 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 and security risks.
• CVE common vulnerabilities and exploits
• Fig. 13 is a flow diagram of an exemplary method 1300 for cybersecurity privilege oversight, according to one aspect.
• time-series data (as described above, referring to Fig. 10) may be collected 1301 for user accounts, credentials, directories, and other user-based privilege and access information.
• This data may then 1302 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 1303 against a CPG (as described previously in Fig. 11), to compare and correlate user directory changes with the actual infrastructure state.
• Fig. 14 is a flow diagram of an exemplary method 1400 for cybersecurity risk management, according to one aspect. According to the aspect, multiple methods described previously may be combined to provide live assessment of attacks as they occur, by first receiving 1401 time-series data for an infrastructure (as described previously, in Fig. 10) to provide live monitoring of network events. This data is then enhanced 1402 with a CPG (as described above in Fig.
• Fig. 15 is a flow diagram of an exemplary method 1500 for mitigating compromised credential threats, according to one aspect.
• impact assessment scores (as described previously, referring to Fig. 9) may be collected 1501 for user accounts in a directory, so that the potential impact of any given credential attack is known in advance of an actual attack event.
• This information may be combined with a CPG 1502 as described previously in Fig. 11, 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 1503 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. 9) may be used in response 1504 to determine the effects of the simulated attack and identify points of weakness, and produce a recommendation report 1505 for improving and hardening the infrastructure against future attacks.
• 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. 16 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 software- 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 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 memory (ROM), including for example one or more levels of cached memory
• RAM non-volatile random access memory
• ROM read-only memory
• 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).
• 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.
• interfaces that may be provided are Ethernet interfaces, frame relay interfaces, cable interfaces, DSL interfaces, token ring interfaces, graphics interfaces, and the like.
• various types of interfaces may be provided such as, for example, universal serial bus (USB), Serial, Ethernet, FIREWIRETM, THUNDERBOLTTM, PCI, parallel, radio frequency (RF),
• USB universal serial bus
• RF radio frequency
• 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.
• interfaces 15 may include physical ports appropriate for communication with appropriate media.
• Fig. 16 illustrates one specific architecture for a computing device 10 for implementing one or more of the aspects described herein, it is by no means the only device architecture on which at least a portion of the features and techniques described herein may be implemented. For example, architectures having one or any number of processors 13 may be used, and such processors 13 may be present in a single device or distributed among any number of devices.
• 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.
• a client device such as a tablet device or smartphone running client software
• 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 nontransitory 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
• flash memory as is common in mobile devices and integrated systems
• SSD solid state drives
• hybrid SSD hybrid SSD
• 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 by 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 aJAVATM compiler and may be executed using ajava 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.
• 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 may be 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.
• Memory 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. 16). 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.
• a distributed computing network such as one having any number of clients and/or servers.
• Fig. 18 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. 17.
• 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 wired 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 may 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 make 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-memory databases, clustered databases, distributed databases, or even flat file data repositories may be used according to the aspect. It will be appreciated by one having ordinary skill in the art that any combination of known 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. Moreover, it should be appreciated that 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.
• Fig. 19 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.
• 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 the Internet.
• power supply unit 45 connected, in this example, to a main alternating current (AC) supply 46.
• AC alternating current
• batteries that could be present, and many other devices and modifications that are well known but are not applicable to the specific novel functions of the current system and method disclosed herein. It should be appreciated that some or all components illustrated may be combined, such as in various integrated applications, for example Qualcomm or Samsung system-on-a-chip (SOC) devices, or whenever it may be appropriate to combine multiple capabilities or functions into a single hardware device (for instance, in mobile devices such as smartphones, video game consoles, in-vehicle computer systems such as navigation or multimedia systems in automobiles, or other integrated hardware devices).
• SOC system-on-a-chip
• 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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