EFFICIENT TRANSFORMER FOR CONTENT-AWARE ANOMALY DETECTION IN EVENT SEQUENCES

    公开(公告)号:US20230252139A1

    公开(公告)日:2023-08-10

    申请号:US18157180

    申请日:2023-01-20

    CPC classification number: G06F21/554

    Abstract: A method for implementing a self-attentive encoder-decoder transformer framework for anomaly detection in event sequences is presented. The method includes feeding event content information into a content-awareness layer to generate event representations, inputting, into an encoder, event sequences of two hierarchies to capture long-term and short-term patterns and to generate feature maps, adding, in the decoder, a special sequence token at a beginning of an input sequence under detection, during a training stage, applying a one-class objective to bound the decoded special sequence token with a reconstruction loss for sequence forecasting using the generated feature maps from the encoder, and during a testing stage, labeling any event representation whose decoded special sequence token lies outside a hypersphere as an anomaly.

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