A Two-stage Hybrid Model for Intrusion Detection

Abstract

As the number of networked computers grows, intrusion detection is an essential component in keeping networks secure. Various approaches to intrusion detection are currently being in use with each one has its own merits and demerits. This paper presents a hybrid approach for modeling intrusion detection system (IDS). Rule based classifier and simple K-means clustering are combined as a hybrid intelligent system. The initial prototype developed by the rule base classifier improves the performance of K-means clustering. The results show that the developed hybrid model provides better IDS.