Analysis of Machine Learning Techniques for Detection System for Web Applications Using Data Mining

Abstract

Security is a key problem to each computer and computer networks. Intrusion detection System (IDS) is one of the most important research problems in community safety. IDSs are advanced to stumble on each acknowledged and unknown assaults. IDS employs many methods to secure information systems and networks against community-based and host-based threats. IDS utilises different machine learning methods. This thesis analyses IDS machine-learning methods. It also discusses several similar research completed between 2000 and 2012 and specialises in engineering techniques. Linked experiments include used unmarried, hybrid, ensemble, baseline, and datasets.