Machine learning techniques for web intrusion detection - a comparison

The rapid development of web applications has created many security problems related to intrusions not just on computer, network systems, but also on web applications themselves. In Web Intrusion Systems (WIS), most techniques used nowadays are not able to deal with the dynamic and complex nature of cyber-attacks on web applications and related issues. However, web intrusion techniques based on machine learning approaches with statistical analysis of data enable autonomous detect intrusive and non-intrusive traffic with low false-positive errors. In this paper, we present the survey of various machine learning techniques used to build WIS. In addition, we develop the experimental framework for comparative analysis of some machine learning techniques applying on the well-known benchmark data set - CSIC 2010 HTTP [13].