Intrusion detection using robust singular value decomposition

    公开(公告)号:US12301598B2

    公开(公告)日:2025-05-13

    申请号:US17446453

    申请日:2021-08-30

    Abstract: A method for detecting anomalous streaming network traffic data in real time includes: creating an anomaly detection model including a singular value matrix and a data pattern matrix from a matrix of historical network traffic data; storing the singular value matrix and the data pattern matrix of the anomaly detection model; receiving streaming network traffic data; performing a log transform on the streaming network traffic data; applying the anomaly detection model to a matrix of the streaming network traffic data in real time as the streaming network traffic data is received; detecting anomalous patterns in the streaming network traffic data based on patterns identified by the anomaly detection model; and associating the anomalous patterns in the streaming network traffic data with IP addresses.

    INTRUSION DETECTION USING ROBUST SINGULAR VALUE DECOMPOSITION

    公开(公告)号:US20210392152A1

    公开(公告)日:2021-12-16

    申请号:US17446453

    申请日:2021-08-30

    Abstract: A method for detecting anomalous streaming network traffic data in real time includes: creating an anomaly detection model including a singular value matrix and a data pattern matrix from a matrix of historical network traffic data; storing the singular value matrix and the data pattern matrix of the anomaly detection model; receiving streaming network traffic data; performing a log transform on the streaming network traffic data; applying the anomaly detection model to a matrix of the streaming network traffic data in real time as the streaming network traffic data is received; detecting anomalous patterns in the streaming network traffic data based on patterns identified by the anomaly detection model; and associating the anomalous patterns in the streaming network traffic data with IP addresses.

    Intrusion detection using robust singular value decomposition

    公开(公告)号:US11108795B2

    公开(公告)日:2021-08-31

    申请号:US15989512

    申请日:2018-05-25

    Abstract: A method for detecting anomalous streaming network traffic data in real time includes: creating an anomaly detection model including a singular value matrix and a data pattern matrix from a matrix of historical network traffic data; storing the singular value matrix and the data pattern matrix of the anomaly detection model; receiving streaming network traffic data; performing a log transform on the streaming network traffic data; applying the anomaly detection model to a matrix of the streaming network traffic data in real time as the streaming network traffic data is received; detecting anomalous patterns in the streaming network traffic data based on patterns identified by the anomaly detection model; and associating the anomalous patterns in the streaming network traffic data with IP addresses.

    Risk identification for unlabeled threats in network traffic

    公开(公告)号:US10958677B2

    公开(公告)日:2021-03-23

    申请号:US16224406

    申请日:2018-12-18

    Inventor: Melissa Lee

    Abstract: A processing system including at least one processor may obtain network traffic data of a network, including a first set of flow data associated with a first node, determine an anomaly factor of the first node from the network traffic data quantifying a deviation of the first set of flow data from a normal flow data associated with the first node, generate an exposure score of the first node in accordance with a measured influence of the first node in the network and the anomaly factor, generate a persistence score of the first node in accordance with a reputation measure of the first node and a measure of a recurrence of anomalous flow data associated with the first node, calculate a threat level of the first node from the exposure score and the persistence score, and reconfigure at least one aspect of the network in response to the threat level.

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