Web application firewall using character-level convolutional neural network - Ito, Michiaki; Iyatomi, Hitoshi (2018 IEEE 14th International Colloquium on Signal Processing & Its Applications (CSPA), 2018)
Web applications can be maliciously exploited by malicious HTTP requests. Normally, web application firewall (WAF) protects web applications from known attacks using pattern matching method. However, introduction of WAF is usually expensive as it requires the definition of patterns according to the situation. Furthermore, the system cannot block unknown malicious request. In this paper, we come up with an efficient machine learning approach to solve these issues. Our approach uses Character-level convolutional neural network (CLCNN) with very large global max-pooling for extracting the feature of HTTP request and identify it into normal or malicious request. We evaluated our system on HTTP DATASET CSIC 2010 dataset and achieved 98.8% of accuracy under 10-fold cross validation and the average processing time per request was 2.35ms.