Abstract:
A server system receives messages from client computing devices. Each of the messages corresponds to a transaction. The server system assigns each respective transaction to a respective fresh virtual machine. Furthermore, the server system performs, as part of a respective virtual machine processing a respective transaction, a modification associated with the respective transaction to a shared database. The shared database is persisted independently of the plurality of virtual machines. In response to determining that processing of the respective transaction is complete, the server system discards the respective virtual machine. In response to a trigger, such as determining that the respective transaction is associated with a cyber-attack, the server system uses checkpoint data associated with the respective transaction to roll back the modifications associated with the respective transaction to the shared database.
Abstract:
A system having a distributed node hardware and software product is disclosed. The distributed topology allows for multiple GPS receiver node positions. The multiple GPS receiver node positions enable an accurate location estimation of a GPS spoofing signal emitter source of an incoming malicious GPS signal. The system detects the presence of a GPS spoofing signal emitter with high confidence against any spoofing geometry or strategy while the GPS receiver nodes are on the move.
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 executes a first attack action by a virtual attack machine against a virtual target machine based on a cyber-attack scenario, wherein the virtual target machine is configured to be controlled by the user computer. The server receives a user response to the first attack action, determines, using a decision tree, a first proposed attack action based on the user response, and executes an artificial intelligence model to determine a second proposed attack action based on the user response. The server selects a subsequent attack action from the first proposed attack action and the second proposed attack action and executes the subsequent attack action by the virtual attack machine against the virtual target machine.
Abstract:
A system includes network nodes, such as, multiple computing devices and multiple software defined radios. The network nodes accurately and timely detects, identifies, locates, and responds to an unmanned aircraft system within a predetermined area. The network nodes use a communications control link between the unmanned aircraft system and a controller of the unmanned aircraft system to detect, identify, locate, and respond to the unmanned aircraft system. The network nodes are deployed over the predetermined area to maintain airspace situational awareness of the unmanned aircraft system, and deploy targeted countermeasures to counteract identified threats associated with the presence of the unmanned aircraft system within the predetermined area.
Abstract:
Disclosed herein are embodiments of systems, methods, and products providing real-time anti-malware detection and protection. The computer uses artificial intelligence techniques to learn and detect new exploits in real time and protect the full system from harm. The computer trains a first machine learning model for executable files. The computer trains a second machine learning model for non-executable files. The computer trains a third machine learning model for network traffic. The computer identifies malware using the various machine learning models. The computer restores to a clean, uncorrupted state using virtual machine technology. The computer reports the detected malware to a security server, such as security information and even management (SIEM) systems, by transmitting detection alert message regarding the malware. The computer interacts with an administrative system over an isolated control network to allow the system administrator to correct the corruption caused by the malware.
Abstract:
Disclosed herein are embodiments of systems, methods, and products for modernizing and optimizing legacy software. A computing device may perform an automated runtime performance profiling process. The performance profiler may automatically profile the legacy software at runtime, monitor the memory usage and module activities of the legacy software, and pinpoint/identify a subset of inefficient functions in the legacy software that scale poorly or otherwise inefficient. The computing device may further perform a source code analysis and refactoring process. The computing device may parse the source code of the subset of inefficient functions and identify code violations within the source code. The computing device may provide one or more refactoring options to optimize the source code. Each refactoring option may comprise a change to the source code configured to correct the code violations. The computing device may refactor the source code based on a selected refactoring option.
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:
In general, this disclosure describes media stream transmission techniques for a computing device. The computing device captures a first media item and identifies a primary portion of the first media item and a secondary portion of the first media item different than the primary portion. The computing device applies a first compression algorithm to the primary portion of the first media item to generate a compressed primary portion. The computing device applies a second compression algorithm to the secondary portion of the first media item to generate a compressed secondary portion, where a data compression ratio of the second compression algorithm is greater than a data compression ratio of the first compression algorithm. The computing device transmits, to a central computing device, the compressed primary portion of the first media item and the compressed secondary portion of the first media item.
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:
A system having a distributed node hardware and software product is disclosed. The distributed topology allows for multiple GPS receiver node positions. The multiple GPS receiver node positions enable an accurate location estimation of a GPS spoofing signal emitter source of an incoming malicious GPS signal. The system detects the presence of a GPS spoofing signal emitter with high confidence against any spoofing geometry or strategy while the GPS receiver nodes are on the move.