Journals Proceedings

Abstract

Intrusion detection systems aim to identify attacks with a high detection rate and a low false alarm rate. Classification-based data mining models for intrusion detection are often ineffective in dealing with dynamic changes in intrusion patterns and characteristics. Consequently, unsupervised learning methods have been given a closer look for network intrusion detection. Traditional instance-based learning methods can only be used to detect known intrusions, since these methods classify instances based on what they have learned. They rarely detect new intrusions since these intrusion classes has not been able to detect new intrusions as well as known intrusions. In this paper, we propose a soft Computing technique such as Self organizing map for detecting the intrusion in network intrusion detection. Problems with k-mean clustering are hard cluster to class assignment, class dominance, and null class problems. The network traffic datasets provided by the NSL-KDD Data set in intrusion detection system which demonstrates the feasibility and promise of unsupervised learning methods for network intrusion detection.