A Comparison of Intrusion Detection by K-Means and Fuzzy C-Means Clustering Algorithm Over the NSL-KDD Dataset

Intrusion detection is the process of identifying intrusions. Intrusion detection system (IDS) is now an essential tool to protect the networks by monitoring inbound and outbound activities and identifying suspicious patterns that may indicate a system attack. In recent years, some researchers have employed data mining techniques for developing IDS. This paper, deals with the evaluation of data mining based machine learning algorithms viz. K-Means and Fuzzy C-Means clustering algorithms to identify intrusion over NSL-KDD dataset for effectively detecting the major attack categories i.e. DoS, R2L, U2R and Probe.