A Performance Evaluation of Intrusion Detection by Fuzzy Possibilistic C-Means Clustering Algorithm over the NSL-KDD Dataset

IDS is a procedure of observing the events happening in a computer system or network and analyzing them for indication of a conceivable potential event which is an infringement or inevitable dangers of infringement or computer security approaches or standard security strategies of adjacent threats. MATLAB 2018a tool is used for the implementation on a NSL-KDD dataset. The motivation behind this investigation is to detect the attack. This paper, deals with the evaluation of data mining based machine learning algorithms viz. Fuzzy C-Means and Fuzzy Possibilistic CMeans clustering algorithms to identify intrusion over NSL-KDD dataset for effectively detecting the major attack categories i.e. DoS, R2L, U2R and Probe.