Research of Intrusion Detection Based on an Improved K-means Algorithm

Traditional machine learning methods for intrusion detection can only detect known attacks since these methods classify data based on what they have learned. New attacks are unknown and are difficult to detect because they have not learned. In this paper, we present an improved k-means clustering-based intrusion detection method, which trains on unlabeled data in order to detect new attacks. The result of experiments run on the KDD Cup 1999 data set shows the improvement in detection rate and decrease in false positive rate and the ability to detect unknown intrusions.