Y-means: a clustering method for intrusion detection

As the Internet spreads to each comer of the world, computers are exposed to miscellaneous intrusions from the World Wide Web. We need effective intrusion detection systems to protect our computers from these unauthorized or malicious actions. Traditional instance-based learning methods for intrusion detection can only detect known intrusions since these methods classify instances based on what they have learned. They rarely detect the intrusions that they have not learned before. In this paper, we present a clustering heuristic for intrusion detection, called Y-means. This proposed heuristic is based on the K-means algorithm and other related clustering algorithms. It overcomes two shortcomings of K-means: number of clusters dependency and degeneracy. The result of simulations run on the KDD-99 data set shows that Y-means is an effective method for partitioning large data space. A detection rate of 89.89% and a false alarm rate of 1.00% are achieved with Y-means.