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公开(公告)号：WO2023064042A1
公开(公告)日：2023-04-20
申请号：PCT/US2022/041416
申请日：2022-08-24
发明人： ŠTERBA, Ján , GOPALAKRISHNAN, Venkatakrishnan , LAM, May Bich Nhi , XUE, Yunjiao , LEI, Nana , CHENG, Edward C. , WELCHER, Hayward Ivan Craig , WEST, Jacob Becker , CAO, Qi Wen
摘要： Machine-learning (ML) techniques and models are described for predicting the number and severity of network attacks within a specified timeframe, such as the next fifteen minutes. In some embodiments, the techniques including training a ML model based on features extracted from a training dataset and applying the trained ML model to estimate (a) the probability of an attack happening on an account within a specified timeframe; (b) how many attacks are predicted to occur in the specified timeframe (if any); and/or (c) the severity of the attacks predicted to occur. A system may deploy preventative measures based on the ML model output to counter or mitigate the effects of predicted and coordinated network attacks.
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公开(公告)号：WO2023075906A1
公开(公告)日：2023-05-04
申请号：PCT/US2022/041413
申请日：2022-08-24
发明人： GOPALAKRISHNAN, Venkatakrishnan , ŠTERBA, Ján , LAM, May Bich Nhi , XUE, Yunjiao , LEI, Nana , CHENG, Edward C. , WELCHER, Hayward Ivan Craig , WEST, Jacob Becker , CAO, Qi Wen
摘要： Machine-learning techniques and models are described for alerting users to attacks on accounts in real-time or near real-time. In some embodiments, an attack detection model uses Natural Language Processing (NLP) and multi-level classification techniques to monitor login attempts and detect attacks. The model may use NLP to convert text associated with account activity to numerical vectors, where the vectors include scores and/or other numerical values computed based on the meaning of the converted text. The model may further include a set of classifiers trained to learn patterns in the numerical vectors that are predictive of a network attack. The model may assign labels to events based on the predicted likelihood that the event is an attack. The system may deploy real-time preventative or corrective measures based on the ML model output to counter or mitigate the effects of an attack.
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