There is described a neural network system implemented by one or more computers for determining graph similarity. The neural network system comprises one or more neural networks configured to process an input graph to generate a node state representation vector for each node of the input graph and an edge representation vector for each edge of the input graph; and process the node state representation vectors and the edge representation vectors to generate a vector representation of the input graph. The neural network system further comprises one or more processors configured to: receive a first graph; receive a second graph; generate a vector representation of the first graph; generate a vector representation of the second graph; determine a similarity score for the first graph and the second graph based upon the vector representations of the first graph and the second graph.

Aspects of the present disclosure are presented for an AI system featuring specially designed AI hardware that incorporates security features to provide iron clad trust and security to run AI applications/solution models. Presented herein are various security features for AI processing, including: a trust and integrity verifier of data during operation of an AI solution model; identity and trust establishment between an entity and the AI solution model; secure isolation for a virtual AI multilane system; a real-time attack detection and prevention mechanism; and built in detection mechanisms related to rogue security attack elements insertion during manufacturing. Aspects also include security to implement an AI network interconnecting multiple user devices in an AI environment.

Systems and methods described herein are directed to upgrading one or more of add-on firmware and disk firmware for a server, which can involve connecting a port of the server to an isolated network, the isolated network dedicated to firmware upgrades for the server; caching onto cache memory of the server, an operating system received through the isolated network; booting the operating system on the server from the cache memory; conducting an Network File System (NFS) mount on the server to determine hardware information associated with the upgrading of the one or more of the add-on firmware and the disk firmware; and upgrading the one or more of the add-on firmware and the disk firmware based on the hardware information.

A virtual environment system for validating executable data using authorized hash outputs is provided. In particular, the system may generate a virtual environment using a virtual environment device, where the virtual environment is logically and/or physically separated from other devices and/or environments within the network. The system may then open a specified set of executable data within the virtual environment and perform a set of commands or processes with respect to the executable data. If the system determines that the executable data is safe to run, the system may generate a hash output of the executable data and store the hash output in a database of approved executable data. In this way, the system may securely generate a repository of authorized hashes such that the system may ensure that only safely executable code is processed by the computing systems within the network environment.

Systems and methods described herein are directed to upgrading one or more of add-on firmware and disk firmware for a server, which can involve connecting a port of the server to an isolated network, the isolated network dedicated to firmware upgrades for the server; caching onto cache memory of the server, an operating system received through the isolated network; booting the operating system on the server from the cache memory; conducting an Network File System (NFS) mount on the server to determine hardware information associated with the upgrading of the one or more of the add-on firmware and the disk firmware; and upgrading the one or more of the add-on firmware and the disk firmware based on the hardware information.

A malware detection system and method detects changes in host behavior indicative of malware execution. The system uses linear discriminant analysis (LDA) for feature extraction, multi-channel change-point detection algorithms to infer malware execution, and a data fusion center (DFC) to combine local decisions into a host-wide diagnosis. The malware detection system includes sensors that monitor the status of a host computer being monitored for malware, a feature extractor that extracts data from the sensors corresponding to predetermined features, local detectors that perform malware detection on each stream of feature data from the feature extractor independently, and a data fusion center that uses the decisions from the local detectors to infer whether the host computer is infected by malware.

Embodiments of the present disclosure provide a method and device for storage management. The method comprises receiving at a storage management device a configuration request for a storage space managed by the storage management device, the configuration request indicating a capacity of the storage space and a target size of a chunk in the storage space; and based on the capacity and the target size, dividing the storage space into a metadata region storing a chunk status indicator indicating whether the chunk is assigned with data and a data region including the chunk with the target size. Embodiments of the present disclosure also provide a corresponding device.

Embodiments of the present disclosure provide a method and device for storage management. The method comprises receiving at a storage management device a configuration request for a storage space managed by the storage management device, the configuration request indicating a capacity of the storage space and a target size of a chunk in the storage space; and based on the capacity and the target size, dividing the storage space into a metadata region storing a chunk status indicator indicating whether the chunk is assigned with data and a data region including the chunk with the target size. Embodiments of the present disclosure also provide a corresponding device.

The present disclosure provides a network architecture and verification platform for analyzing the various modules of a Unified Extensible Firmware Interface (UEFI) firmware image. In one embodiment, the disclosed network architecture and verification platform obtains various UEFI firmware images, such as UEFI firmware image residing on a client device or a UEFI firmware image hosted by a hardware manufacturer. The network architecture and verification platform may then segregate the various UEFI firmware modules that make up the UEFI firmware image, and subject the modules to different types of analysis. By analyzing the UEFI firmware modules individually, the network architecture and verification platform builds a repository of Globally Unique Identifiers (GUIDs) referenced by a given UEFI firmware module, which may then be referenced in future analyses to determine whether any changes, and the extent of such changes, have been made to an updated version of the given UEFI firmware module.

Testing a computer software application by detecting an arrival of input data provided as input to a computer software application from a source external to the computer software application, modifying the detected input data to include test data configured to test the computer software application in accordance with a predefined test, thereby creating a modified version of the detected input data, and processing the modified version of the detected input data, thereby performing the predefined test on the computer software application using the test data.