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. The example method further includes converting the platform-independent instructions into platform-dependent instructions, wherein the platform-dependent instructions have a format that is specific to the selected processing unit, and executing, by the selected processing unit, the platform-dependent instructions to perform the at least one computational task.
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. The example method further includes converting the platform-independent instructions into platform-dependent instructions, wherein the platform-dependent instructions have a format that is specific to the selected processing unit, and executing, by the selected processing unit, the platform-dependent instructions to perform the at least one computational task.
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 network defense system is described that provides network sensor infrastructure and a framework for managing and executing advanced cyber security algorithms specialized for detecting highly-distributed, stealth network attacks. In one example, a system includes a data collection and storage subsystem that provides a central repository to store network traffic data received from sensors positioned within geographically separate networks. Cyber defense algorithms analyze the network traffic data and detect centrally-controlled malware that is configured to perform distributed network attacks (“botnet attacks”) from devices within the geographically separate networks. A visualization and decision-making subsystem generates a user interface that presents an electronic map of geographic locations of source devices and target devices of the botnet attacks. The data collection and storage subsystem stores a manifest of parameters for the network traffic data to be analyzed by each of the cyber defense algorithms.
Abstract:
A plurality of distributed network nodes may provide a decentralized access gateway to multiple, diverse types of databases. The plurality of distributed network nodes may host a private party blockchain. Each node may execute a peer-to-peer (P2P) client to perform operations associated with the private party blockchain. A subset of the nodes may be configured as validator nodes that may implement gossip protocols to cooperatively validate one or more database operations and generate a new block for the private party blockchain. Another subset of nodes may be configured as host nodes that may receive the new block and update a corresponding local copy of the private party blockchain appending the new block. Utilizing the co-operative validation of database operations and the updates appending the new blocks, the private party blockchain may maintain an immutable digital record of access and updates to the multiple and diverse types of databases.
Abstract:
Embodiments disclosed herein describe systems and methods for assessing vulnerabilities of embedded non-IP devices. In an illustrative embodiment, a system of assessing the vulnerabilities of embedded non-IP devices may be within a portable device. The portable device may include a plurality of wired connectors for various wired communication/data transfer protocols. The portable device may include tools for analyzing the firmware binaries of the embedded non-IP devices, such as disassemblers and modules for concrete and symbolic (concolic) execution. Based upon the disassembly and the concolic execution, the portable device may identify vulnerabilities such as buffer overflows and programming flaws in the firmware binaries.
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:
Various embodiments described herein relate to a machine-learning based electronic media analysis software system. The system is configured to detect anomalous and predictive patterns associated with an event. The system is configured to use feature extraction techniques and semi-supervised machine-learning to detect the patterns associated with the event in the electronic media messages, which may indicate a synthetic driven behavior and conversation corresponding to the event.
Abstract:
In general, this disclosure describes methods and devices for analyzing source code to detect potential bugs in the code. Specifically, a device retrieves source code of an application. For each distinct execution of a plurality of executions of the application, the device initiates the respective execution at a particular starting point of the source code and inputs, into the source code, a unique set of inputs relative to any other execution. The device stores, into a path log, an indication of each line of source code and stores, into an output log, an indication of each output object encountered during the respective execution. Each output object includes a local variable dependent on the inputs. The device analyzes, using a machine learning model, the path and output logs to identify an abnormality indicative of a potential bug in the source code. The device outputs a graphical representation of the abnormality.
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.