NETWORK SECURITY MONITORING AND CORRELATION SYSTEM AND METHOD OF USING SAME

    公开(公告)号:US20230344731A1

    公开(公告)日:2023-10-26

    申请号:US18345050

    申请日:2023-06-30

    Abstract: A network security monitoring and correlation system for providing a three-dimensional visualization of network traffic overlaid with security alerts and other relevant discrete data. The system may comprise an application server communicably linked to a client. The server functions to retrieve network traffic metadata and relevant discrete data associated with individual computer hosts and connections in the monitored network, process the network traffic data by building a graph data structure, and then embedding within the graph data structure one or more layers of additional information about the individual computer hosts and connections derived from the discrete data. The client functions to produce a three-dimensional visualization of the network environment by parsing the graph data structure received from the server and then spawning computer hosts and connections in the 3-D environment. The client will then add the overlay information to the appropriate hosts or connections, with the overlay information preferably being represented within the 3-D environment as a particular color, shape, size, position, or a changing dynamic value.

    Optimization Method and Server Thereof
    72.
    发明公开

    公开(公告)号:US20230344730A1

    公开(公告)日:2023-10-26

    申请号:US17869764

    申请日:2022-07-20

    Inventor: Chih-Ming Chen

    CPC classification number: H04L43/045 H04L41/0823

    Abstract: An optimization method includes generating a constrained causal graph according to an observation data received from a distributed unit, performing a finite domain representation planning using the constrained causal graph to generate an action data about a plurality of radio unit parameters after optimization, and outputting the action data to the distributed unit. A number of a plurality of causal variables of the constrained causal graph and a causal structure of the constrained causal graph are determined at a time.

    Systems and methods for investigating potential incidents across entities in networked environments

    公开(公告)号:US11799736B2

    公开(公告)日:2023-10-24

    申请号:US16729115

    申请日:2019-12-27

    CPC classification number: H04L41/22 G06F3/0482 H04L41/14 H04L43/045

    Abstract: Provided herein are systems and methods of investigating an entity or a potential incident. A tracker engine may receive an identification of a first entity in a networked environment. The tracker engine may display, in a user interface responsive to receiving the identification, a representation of the first entity, and representations of a plurality of entities associated with the first entity. The plurality of associated entities may include: a network connection, a file, a process, a user or a computing device. The tracker engine may receive, via the user interface, a selection of a second entity from the plurality of associated entities. The tracker engine may update, responsive to receiving the selection, the user interface to display a representation of the second entity graphically linked to the representation of the first entity, and representations of a plurality of entities associated with the second entity.

    Artificial intelligence based device identification

    公开(公告)号:US11770315B2

    公开(公告)日:2023-09-26

    申请号:US17997851

    申请日:2020-05-28

    CPC classification number: H04L43/062 H04L41/16 H04L43/045

    Abstract: A system for obtaining information about an Internet of Things (IoT) device connected to a network includes a data traffic collection point, a data processing module and an artificial intelligence module. The data traffic collection point collects data units communicated to or from a specified IoT device of a plurality of IoT devices connected to a network. The data processing module processes quantitative information pertaining to the data units communicated to or from the specified IoT device collected over a defined time interval, to create a temporal data traffic fingerprint of the specified IoT device. The artificial intelligence module utilizes a machine learning model to deduce device identifying information of the specified IoT device from the temporal data traffic fingerprint of the specified IoT device.

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