Abstract: Disclosed herein are embodiments of systems, methods, and products that provide adversary detection and threat hunting. A server may comprise a user side virtual machine facing the cyber protection users, a collection virtual machine facing the at-risk network, and a data repository. The server may receive user requests requesting status data from the at-risk network via the user side virtual machine. The server may collect status data from the at-risk network via the collection virtual machine and store the collected data into the data repository. Different users may request duplicate information from the at-risk network. The server may retrieve the requested information from the data repository for duplicate requests and return the responses immediately for such requests. Because the server does not query the at-risk network for duplicate requests, the server may reduce the amount of bandwidth needed to acquire and distribute the requested information.

Abstract: The methods and systems disclosed herein generally relate to automated execution and evaluation of computer network training exercises, such as in a virtual environment. A server generates a training system having a virtual attack machine and a virtual target machine where the virtual target machine is operatively controlled by a trainee computer. The server then executes a simulated cyber-attack and monitors/collects actions and responses by the trainee. The server then executes an artificial intelligence model to evaluate the trainee's action and to identify a subsequent simulated cyber-attack (e.g., a next step to the simulated cyber-attack). The server may then train the artificial intelligence model using various machine-learning techniques using the collected data during the exercise.

Abstract: Disclosed herein are embodiments of systems, methods, and products comprise an analytic server, which provides a terrain segmentation and classification tool for synthetic aperture radar (SAR) imagery. The server accurately segments and classifies terrain types in SAR imagery and automatically adapts to new radar sensors data. The server receives a first SAR imagery and trains an autoencoder based on the first SAR imagery to generate learned representations of the first SAR imagery. The server trains a classifier based on labeled data of the first SAR imagery data to recognize terrain types from the learned representations of the first SAR imagery. The server receives a terrain query for a second SAR imagery. The server translates the second imagery data into the first imagery data and classifies the second SAR imagery terrain types using the classifier trained for the first SAR imagery. By reusing the original classifier, the server improves system efficiency.

Abstract: Disclosed herein are embodiments of systems, methods, and products comprise an analytic server, which improves the cybersecurity of a unified system comprising a plurality of sub-systems. The analytic server may instantiate a sub attack tree for each network sub-system within the unified system of distributed network infrastructure. The analytic server may access the sub attack trees of the network sub-systems based on the corresponding identifiers. The analytic server may build a high-level attack tree of the unified system by aggregating the sub attack tree of each sub-system. The analytic server may determine how the interconnection of the plurality of network sub-systems may affect the unified system security. The analytic server may update one or more nodes of the attack tree to reflect the changes produced from the interconnection. The analytic server may build the attack tree based on a set of aggregation rules.

Abstract: An example technique includes initializing, by an obfuscation computing system, communications with nodes in a distributed computing platform. The nodes include compute nodes that provide resources in the distributed computing platform and a controller node that performs resource management of the resources. The obfuscation computing system serves as an intermediary between the controller node and the compute nodes. The technique further includes outputting an interactive user interface (UI) providing a selection between a first privilege level and a second privilege level, and performing one of: based on the selection being for the first privilege level, a first obfuscation mechanism for the distributed computing platform to obfuscate digital traffic between a user computing system and the nodes, or based on the selection being for the second privilege level, a second obfuscation mechanism for the distributed computing platform to obfuscate digital traffic between the user computing system and the nodes.

Abstract: This disclosure provides example techniques to invoke one or more tools, with an investigative tool. The investigative tool provides a common framework that allows investigators to invoke their own trusted tools or third-party generated tools. The investigative tool described herein seamlessly and transparently invokes the tools in accordance with an investigative profile created by the investigator.

Abstract: An example method includes monitoring execution of one or more applications on a runtime computing system that includes a plurality of processing units, receiving, from the runtime computing system during execution of the applications, monitoring information that includes at least one of function call data or application programming interface call data associated with operations performed by the plurality of processing units during execution of the applications, importing the monitoring information into a risk model, analyzing the monitoring information within the risk model to determine one or more potential vulnerabilities and one or more impacts of the one or more vulnerabilities in the runtime computing system, and outputting, for display in a graphical user interface, a graphical representation of the one or more potential vulnerabilities and the one or more impacts within the risk model.

Abstract: An example method includes storing a scenario event list that defines one or more events associated with a training exercise, and configuring, based on the events defined in the scenario event list, one or more software agents to emulate one or more cyber-attacks against a host computing system during the training exercise, which includes configuring the software agents to save a state of one or more resources of the host computing system prior to emulating the cyber-attacks and to restore the state of the resources upon conclusion of the cyber-attacks. The example method further includes deploying the software agents for execution on the host computing system during the training exercise to emulate the cyber-attacks against the host computing system using one or more operational networks.

Abstract: Disclosed herein are embodiments of systems and methods that dynamically reconfigure a multi-tiered system of network devices and software applications in response to an ongoing and/or anticipated cyber-attack. The dynamic reconfiguration of the network devices may consist of a wide range of processes, which may include generating new network addresses for individual network devices; reconfiguring the network devices by creating firewalls, changing protocols between the network devices in a multi-tier reconfiguration solution, changing the cloud infrastructure provider of the network devices, even when the underlying network infrastructure ecosystem differs across cloud service providers (CSPs); and maintaining a secure and updated data model of a record of reconfigured network devices and their dependencies to allow legitimate users of the network devices to understand reconfiguration actions that are hidden from malicious users such as hackers and cyber-attackers.

Abstract: An example network security and threat assessment system is configured to determine, based on one or more events that have occurred during execution of one or more applications, a potential security vulnerability of a target computing system, where the one or more events correspond to a node represented in the hierarchical risk model. The system is further configured to identify, based on a mapping of the node represented in the hierarchical risk model to a node represented in a hierarchical game tree model, one or more actions that are associated with the potential security vulnerability and that correspond to the node represented in the hierarchical game tree model, and to output, for display in a graphical user interface, a graphical representation of the potential security vulnerability and the one or more actions associated with the potential security vulnerability.

Abstract: Disclosed herein are embodiments of systems, methods, and products comprise an analytic server, which detects and defends against malware in-flight regardless of the specific nature and methodology of the underlying attack. The analytic server learns the system's normal behavior during testing and evaluation phase and trains a machine-learning model based on the normal behavior. The analytic server monitors the system behavior during runtime comprising the runtime behavior of each sub-system of the system. The analytic server executes the machine-learning model and compares the system runtime behavior with the normal behavior to identify anomalous behavior. The analytic server executes one or more mitigation instructions to mitigate malware. Based on multiple available options for mitigating malware, the analytic server makes an intelligent decision and takes the least impactful action that have the least impact on the system to maintain mission assurance.

Abstract: Disclosed herein are embodiments of systems, methods, and products that execute tools to identify non-malicious faults in source codes introduced by engineers and programmers. The tools may execute a machine learning model on the source codes to perform sentiment analysis and pattern analysis on information associated with the source codes to generate annotated source code files identifying anomalies based on the sentiment analysis and the pattern analysis. One or more threat levels are then identified and ranked based on the one or more anomalies and a ranked list of the one or more threat levels is displayed on a graphical user interface of a computer.

Abstract: Disclosed herein are embodiments of systems, methods, and products comprise an analytic server, which provides a SilverlineRT system that prioritizes and analyzes security alerts and events. The server builds an attack tree based on attack detection rules. The server monitors large-scale distributed systems and receives alerts from various devices. The server determines attacks using the attack tree while excluding false alarms. The server determines impact and risk metrics for attacks in real-time, and calculates an impact score for each attack. The server ranks and prioritizes the attacks based on the impact scores. The server also generates real-time reports. By consider the mission and system specific context in the analysis alert information, the server gives insight into the overall context of problems and potential solutions, improving decision-making. By showing the impacts of alters, the server allows security personnel to prioritize responses and focus on highest value defense activities.

Abstract: Disclosed herein are embodiments of systems, methods, and products comprise an analytic server, which provides a terrain segmentation and classification tool for synthetic aperture radar (SAR) imagery. The server accurately segments and classifies terrain types in SAR imagery and automatically adapts to new radar sensors data. The server receives a first SAR imagery and trains an autoencoder based on the first SAR imagery to generate learned representations of the first SAR imagery. The server trains a classifier based on labeled data of the first SAR imagery data to recognize terrain types from the learned representations of the first SAR imagery. The server receives a terrain query for a second SAR imagery. The server translates the second imagery data into the first imagery data and classifies the second SAR imagery terrain types using the classifier trained for the first SAR imagery. By reusing the original classifier, the server improves system efficiency.

Abstract: For each respective virtual machine (VM) of a plurality of VMs, a distributed computing system generates a unique Application Binary Interface (ABI) for an operating system for the respective VM, compiles a software application to use the unique ABI, and installs the operating system and the compiled software application on the respective VM. A dispatcher node dispatches, to one or more VMs of the plurality of VMs that provide a service and are in the active mode, request messages for the service. Furthermore, a first host device may determine, in response to software in the first VM invoking a system call in a manner inconsistent with the unique ABI for the operating system of the first VM, that a failover event has occurred. Responsive to the failover event, the distributed computing system fails over from the first VM to a second VM.

Abstract: An example method includes initializing, by an obfuscation computing system, communications with nodes in a distributed computing platform, the nodes including one or more compute nodes and a controller node, and performing at least one of: (a) code-level obfuscation for the distributed computing platform to obfuscate interactions between an external user computing system and the nodes, wherein performing the code-level obfuscation comprises obfuscating data associated with one or more commands provided by the user computing system and sending one or more obfuscated commands to at least one of the nodes in the distributed computing platform; or (b) system-level obfuscation for the distributed computing platform, wherein performing the system-level obfuscation comprises at least one of obfuscating system management tasks that are performed to manage the nodes or obfuscating network traffic data that is exchanged between the nodes.

Abstract: An example method includes monitoring execution of one or more applications on a runtime computing system that includes a plurality of processing units, receiving, from the runtime computing system during execution of the applications, monitoring information that includes at least one of function call data or application programming interface call data associated with operations performed by the plurality of processing units during execution of the applications, importing the monitoring information into a risk model, analyzing the monitoring information within the risk model to determine one or more potential vulnerabilities and one or more impacts of the one or more vulnerabilities in the runtime computing system, and outputting, for display in a graphical user interface, a graphical representation of the one or more potential vulnerabilities and the one or more impacts within the risk model.

Abstract: For each respective virtual machine (VM) of a plurality of VMs, a distributed computing system generates a unique Application Binary Interface (ABI) for an operating system for the respective VM, compiles a software application to use the unique ABI, and installs the operating system and the compiled software application on the respective VM. A dispatcher node dispatches, to one or more VMs of the plurality of VMs that provide a service and are in the active mode, request messages for the service. Furthermore, a first host device may determine, in response to software in the first VM invoking a system call in a manner inconsistent with the unique ABI for the operating system of the first VM, that a failover event has occurred. Responsive to the failover event, the distributed computing system fails over from the first VM to a second VM.

Abstract: A risk model for a distributed computing system comprises a plurality of tree nodes organized as a tree. For each tree node of the risk model, the tree node corresponds to a respective event that may befall a distributed computing system. An analysis computing system generates data associating a test agent with a target and also generates data associating the test agent with a tree node in the risk model. The test agent performs a data gathering routine that gathers data from the target associated with the test agent. The gathered data may indicate whether the event corresponding to the tree node is occurring. Furthermore, the analysis computing system may perform the data gathering routine according to a recurrence pattern of the data gathering routine. The analysis computing system may output a graphical representation of the data indicating whether the event corresponding to the tree node is occurring.

Abstract: An example method includes providing, by a computing system, first randomized configuration information, generating, by the computing system and based on the first randomized configuration information, a first unique instance of a software component, providing second randomized configuration information, wherein the second randomized configuration information is different from the first randomized configuration information, and generating, based on the second randomized configuration information, a second unique instance of the software component that is executable on the runtime computing system.