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This paper proposes an anomaly detection model based on the reconstruction error to detect malicious requests in a Web application. Our model combines a multi-head attention network and gated convolution network to capture the pattern of a normal request. Moreover, we use a novel segmentation method to enhance the structural representation of a request and embed a raw request into a feature matrix. The result of this experiment indicates that our model has good ability to distinguish between normal and abnormal requests.

Web Application Attack Detection Based on Attention and Gated Convolution Networks