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Web Application Attack Detection Based on Attention and Gated Convolution Networks
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DOI：10.1109/ACCESS.2019.2955674.png)
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摘要
En 中文
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.
Keyword:
Anomaly detection
Feature extraction
Logic gates
Convolution
Computational modeling
Uniform resource locators
Task analysis
Anomaly detection
web application attack
multi-head attention
gated convolution
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期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
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