Research on Network Intrusion Detection System Based on Improved K-means Clustering Algorithm
With the development of computer technology, network security has become an important issue of concern. In view of the growing number of network security threats and the current intrusion detection system development, this paper gives a new model of anomaly intrusion detection based on clustering algorithm. Because of the k-means algorithms shortcomings about dependence and complexity, the paper puts forward an improved clustering algorithm through studying on th traditional means clustering algorithm. The new algorithm learns the strong points from the k-medoids and improved relations trilateral triangle theorem. The experiments proved that the new algorithm could improve accuracy of data classification and detection efficiency significantly. The results show that this algorithm achieves the desired objectives with a high detection rate and high efficiency.
network security network intrusion detection systemt clustering algorithm k-means algorithm abnormal detection
Li Tian Wang Jianwen
Department of Computer Science, North China Electric Power University (NCEPU), Baoding 071003, China
国际会议
重庆
英文
76-79
2009-12-25（万方平台首次上网日期，不代表论文的发表时间）