Automatic Detection of HTTP Injection Attacks using Convolutional Neural Network and Deep Neural Network

HTTP injection attacks are well known cyber security threats with fatal consequences. These initiated by malicious entities (either human or computer) send dangerous unsafe contents into the parameters of requests. Combatting demands for development Web Intrusion Detection Systems (WIDS). Common WIDS follow a rule-based approach signature-based which have common problem high false-positive rate (wrongly classifying requests) hence making them restricted to only one type web application. They easily bypassed and unable detect new kinds as they lack sufficient model understanding representations request parameters. In this paper, deep learning techniques used develop models that would automatically in A special layer called character embedding is allow representation parameter requests higher abstract levels also aid relationships between characters parameter. The experimentation results showed learning, better attack detection possible given right dataset, be able correctly classify any