Fuzzy K-Means Based Intrusion Detection System Using Support Vector Machine
Abstract: 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.
Keywords: Intrusion Detection, Fuzzy K-Mean, SVM
How to Cite?: Aman Mudgal, Rajiv Munjal, "Fuzzy K-Means Based Intrusion Detection System Using Support Vector Machine", Volume 3 Issue 8, August 2014, International Journal of Science and Research (IJSR), Pages: 1307-1310, https://www.ijsr.net/getabstract.php?paperid=2015689, DOI: https://dx.doi.org/10.21275/2015689