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    HIGH-CONFIDENCE MALWARE SEVERITY CLASSIFICATION OF REFERENCE FILE SET
    1.
    发明申请
    HIGH-CONFIDENCE MALWARE SEVERITY CLASSIFICATION OF REFERENCE FILE SET 有权

    公开(公告)号：US20220229906A1

    公开(公告)日：2022-07-21

    申请号：US17151462

    申请日：2021-01-18

    Applicant: Avast Software s.r.o.

    Inventor： Martin Bálek ,  Fabrizio Biondi ,  Dmitry Kuznetsov ,  Olga Petrova

    IPC: G06F21/56 ,  G06F21/54 ,  G06K9/62 ,  G06N20/00

    Abstract: A reference file set having high-confidence malware severity classification is generated by selecting a subset of files from a group of files first observed during a recent observation period and including them in the subset. A plurality of other antivirus providers are polled for their third-party classification of the files in the subset and for their third-party classification of a plurality of files from the group of files not in the subset. A malware severity classification is determined for the files in the subset by aggregating the polled classifications from the other antivirus providers for the files in the subset after a stabilization period of time, and one or more files having a third-party classification from at least one of the polled other antivirus providers that changed during the stabilization period to the subset are added to the subset.

    High-confidence malware severity classification of reference file set
    2.
    发明授权
    High-confidence malware severity classification of reference file set 有权

    公开(公告)号：US11861006B2

    公开(公告)日：2024-01-02

    申请号：US17151462

    申请日：2021-01-18

    Applicant: Avast Software s.r.o.

    Inventor： Martin Bálek ,  Fabrizio Biondi ,  Dmitry Kuznetsov ,  Olga Petrova

    IPC: H04L29/06 ,  G06F21/56 ,  G06N20/00 ,  G06F21/54 ,  G06F18/21

    CPC classification number: G06F21/566 ,  G06F18/217 ,  G06F21/54 ,  G06F21/568 ,  G06N20/00

    Abstract: A reference file set having high-confidence malware severity classification is generated by selecting a subset of files from a group of files first observed during a recent observation period and including them in the subset. A plurality of other antivirus providers are polled for their third-party classification of the files in the subset and for their third-party classification of a plurality of files from the group of files not in the subset. A malware severity classification is determined for the files in the subset by aggregating the polled classifications from the other antivirus providers for the files in the subset after a stabilization period of time, and one or more files having a third-party classification from at least one of the polled other antivirus providers that changed during the stabilization period to the subset are added to the subset.

    MALICIOUS PATTERN MATCHING USING GRAPH NEURAL NETWORKS
    4.
    发明公开
    MALICIOUS PATTERN MATCHING USING GRAPH NEURAL NETWORKS 审中-公开

    公开(公告)号：US20240354406A1

    公开(公告)日：2024-10-24

    申请号：US18305940

    申请日：2023-04-24

    Applicant: Avast Software s.r.o.

    Inventor： Václav Belák ,  Martin Bálek ,  Tomáš Strenácik ,  Bretislav Šopík

    IPC: G06F21/55 ,  G06N3/08

    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.

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