Fuzzy K-Means Based Intrusion Detection System Using Support Vector Machine

Intrusion Detection System (IDS) is an important tool to identify various attacks to secure the networks. The goal of an Intrusion Detection System (IDS) is to provide a layer of defense against malicious users of computer systems by sensing a misuse and alerting operators to on-going attacks. Most real-world data, especially data available on the web, possess rich structural relationships. Most of the clustering algorithms neglect the structural relationships between the individual data types. We proposed Fuzzy K-Means clustering, which integrates two sources of information into a single clustering framework. Our main objective is to complete analysis of intrusion detection Dataset. In this paper we combine two of the efficient data mining algorithms and make a hybrid technique for the detection of intrusion called fuzzy k-means and Support vector machine.