Achieving Interpretable DL-based Web Attack Detection through Malicious Payload Localization
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Presented at NDSS 2026 by
Web attacks pose a significant threat to Web applications. While deep learning-based systems have emerged as promising solutions for detecting Web attacks, the lack of interpretability hinders their deployment in production. Existing interpretability methods are unable to explain Web attacks because they overlook the structure information of HTTP requests. They merely identify some important features, which are not understandable by security operators and fail to guide them toward effective responses.