Algorithm for clustering with intrusion detection using modified and hashed K - Means algorithms

Abstract

The k-Means clustering algorithm partition a dataset into meaningful patterns. Intrusion Detection System detects malicious attacks which generally include theft information. It can be found from the studies that clustering based intrusion detection methods may be helpful in detecting unknown attack patterns compared to traditional intrusion detection systems. This paper presents modified k-Means by applying preprocessing and normalization steps. As a result the effectiveness is improved and it overcomes the shortcomings of k-Means. This approach is proposed to work on network intrusion data and the algorithm is experimented with KDD99 dataset and found satisfactory results. © 2012 Springer-Verlag GmbH.