  Modern web applications are dominated by HTTP/HTTPS messages that consist of
one or more headers, where most of the exploits and payloads can be injected by
attackers. According to the OWASP, the 80 percent of the web attacks are done
through HTTP/HTTPS requests queries. In this paper, we present a deep learning
based web application attacks detection model. The model uses auto-encoder that
can learn from the sequences of word and weight each word or character
according to them. The classification engine is trained on ECML-KDD dataset for
classification of anomaly queries with respect to specific attack type. The
proposed web application detection engine is trained with anomaly and benign
web queries to achieve the accuracy of receiver operating characteristic curve
of 1. The experimental results show that the proposed model can detect web
applications attack successfully with low false positive rate.
