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: In general, this disclosure describes media stream transmission techniques for a computing device. The computing device may capture an image of a local background environment. The computing device may record a first media stream that includes at least a portion of the image of the background environment and at least one movement of at least one object through the background environment. The computing device may remove the image of the background environment from the first media stream to create a second media stream that includes the movement of the object without the image of the background environment. The computing device may determine a bandwidth of a network over which the second media stream will be transmitted and perform further alterations to the second media stream if the current bandwidth is less than a bandwidth threshold level in order to reduce the bandwidth needed to transmit the second media stream.

Abstract: An integrated adaptive learning system provides the functions of scenario development, exercise execution, exercise monitoring, exercise evaluation, exercise adaptation, and scenario feedback, which may be implemented in one or more of a scenario builder module, an exercise test module, an exercise controller module, an exercise performance monitor module, an exercise evaluation module, an exercise adaption module, and an exercise reporting and scenario feedback module. The modules, and other components of the integrated adaptive learning system may be implemented as a standalone physical training system, a training overlay to an existing physical system, and a virtual training system, or combinations thereof. In an aspect, the integrated adaptive learning system may be implemented as a physical or a virtual variable-fidelity cyber range.

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: In general, the disclosure is directed to data storage within a peer-to-peer network that includes a plurality of computing devices. A first computing device of the peer-to-peer network stores an encrypted file in a data storage component. The first computing device creates file information metadata comprising details of the encrypted file and peer information metadata comprising details of the first computing device. The first computing device updates a file distributed hash table to include the file information metadata and a peer distributed hash table to include the peer information metadata. At least a portion of the file distributed hash table is stored on a first group of one or more computing devices of the plurality of computing devices. Further, at least a portion of the peer distributed hash table is stored on a second group of one or more computing devices of the plurality of computing devices.

Abstract: Described herein are various computing technologies for various reverse engineering platforms capable of outputting (e.g., creating, generating) a human readable and high level source code (e.g., C formatted text, Fortran formatted text, LISP formatted text, BASIC formatted text) from various binary files (e.g., application binary file, executable binary file, data binary file) in its original language, as developed before compilation. For example, some of such reverse engineering platforms can be programmed to disassemble binary files from different process architectures, identify various code optimizations as compiler introduced, reverse or unwind various compiler optimizations (de-optimize), and generate a human readable and high-level source code from de-optimized data. These techniques can be technically beneficial for design recovery purposes (e.g., security auditing, digital rights management, driver engineering).

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: 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.

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.

Abstract: An example method includes generating, by a computing system, first unique configuration information, generating, by the computing system and based on the first unique configuration information, a first unique instance of a software component, generating second unique configuration information, wherein the second unique configuration information is different from the first unique configuration information, and generating, based on the second unique configuration information, a second unique instance of the software component that is executable on the runtime computing system. The first and second unique instances of the software component comprise different instances of the same software component that each are configured to have uniquely different operating characteristics during execution on the runtime computing system.

Abstract: An example method includes, during execution of a software application in a computing system comprising a plurality of processing units, identifying platform-independent instructions that are configured to perform at least one computational task, wherein the plurality of processing units comprises a heterogeneous group, and wherein the platform-independent instructions have a format that is not specific to any particular processing unit in the plurality of processing units, determining one or more scheduling criteria that are associated with the platform-independent instructions, and selecting, from the heterogeneous group of processing units and based on the scheduling criteria, a processing unit to perform the at least one computational task.

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: An example method includes selecting, based at least on first and second policies, first and second containers in which to execute first and second applications, respectively. The example method further includes isolating execution of the first application in the first container, and isolating execution of the second application in the second container. The example method also includes applying, based at least on the first policy, a first group of security controls to the first application executing in the first container, wherein the first container defines a first domain in which the first application is executed, and applying, based at least on the second policy, a second group of security controls to the second application executing in the second container, wherein the second container defines a second domain in which the second application is executed.

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.

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.

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.