PREDICTING FUTURE MALWARE WITH GENERATIVE MODELS

    公开(公告)号:US20230130651A1

    公开(公告)日:2023-04-27

    申请号:US17511305

    申请日:2021-10-26

    Abstract: A malware classification system includes a first machine-learning model trained based on malware from a first plurality of prior time periods to predict malware in a first subsequent time period subsequent to the first plurality of prior time periods, and a second machine-learning model is trained based on malware from a second plurality of prior time periods offset by at least some time from the plurality of time periods used to train the first machine-learning model to predict malware in a second subsequent time period subsequent to the second plurality of prior time periods. The trained first and second machine-learning models are used to predict malware in a future time period, and a classifier is trained using the malware from a plurality of the prior time periods and predicted malware from a future time period to train the classifier to identify and/or classify malware.

    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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