MALICIOUS PATTERN MATCHING USING GRAPH NEURAL NETWORKS

    公开(公告)号:US20240354406A1

    公开(公告)日:2024-10-24

    申请号:US18305940

    申请日:2023-04-24

    CPC classification number: G06F21/554 G06N3/08 G06F2221/034

    Abstract: A method of detecting likely malicious activity in a sequence of computer instructions includes identifying a set of behaviors of the computer instructions and representing the identified behaviors as a graph. The graph is provided to a graph neural network that is trained to generate a geometric representation of the sequence of computer instructions, and a degree of relatedness between the geometric representation of the computer instructions and a set of base graphs including base graphs known to be malicious is determined. The sequence of computer instructions is determined to likely be malicious or clean based on a degree of relatedness between the geometric representation of the computer instructions and one or more base graphs known to be malicious.

    SYSTEMS AND METHODS FOR DETECTING AND MITIGATING THREATS IN ELECTRONIC MESSAGES

    公开(公告)号:US20250097263A1

    公开(公告)日:2025-03-20

    申请号:US18469117

    申请日:2023-09-18

    Abstract: Systems and methods enable a notification based on determining a particular electronic message is associated with a particular cluster of electronic messages. A plurality of electronic messages from a first plurality of accounts directed to a second plurality of accounts over a network are received. The plurality of electronic messages are compared to determine a plurality of clusters of electronic messages. A particular electronic message is received from a first particular account directed to a second particular account. The particular electronic message is compared to the plurality of clusters of electronic messages to determine that the particular electronic message is associated with a particular cluster of the plurality of clusters of electronic messages. A notification is provided based on the determining that the particular electronic message is associated with the particular cluster of the plurality of clusters of electronic messages.

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