An Improved Feature Extraction Approach For Web Anomaly Detection Based On Semantic Structure

Anomaly-based Web application firewalls (WAFs) are vital for providing early reactions to novel Web attacks. In recent years, various machine learning, deep learning, and transfer learning-based anomaly detection approaches have been developed to protect against Web attacks. Most of them directly treat the request URL as a general string that consists of letters and roughly use natural language processing (NLP) methods (i.e., Word2Vec and Doc2Vec) or domain knowledge to extract features. In this paper, we proposed an improved feature extraction approach which leveraged the advantage of the semantic structure of URLs. Semantic structure is an inherent interpretative property of the URL that identifies the function and vulnerability of each part in the URL. The evaluations on CSIC-2020 show that our feature extraction method has better performance than conventional feature extraction routine by more than average dramatic 5% improvement in accuracy, recall, and F1-score.