Performance analysis of machine learning techniques in intrusion detection

With computer and Internet to be an indispensable part of our daily lives, the number of Web applications on the Internet has increased rapidly. With the increasing number of Web applications, attacks on the disclosure of data on the internet and the number of varieties has increased. Made over the Web attacks and to detect unauthorized access requests, intrusion detection systems have been used successfully. In this study, In order to develop a more efficient STS, machine learning techniques, Bayesian networks, support vector machines, neural networks, k nearest neighbor algorithm and decision trees examined the success of the STS, the success and process time of the classifier according to the types of attacks have been analyzed. Kddcup99 data sets were used in experimental studies.