Using K-means Clustering Algorithms for Anomaly Detection

• Main Authors: Yi-Fen Huang, 黃奕棻
• Other Authors: Zone-Chang Lai
• Format: Others
• Language: zh-TW
• Published: 2009
• Online Access: http://ndltd.ncl.edu.tw/handle/50724702843472906729

碩士 === 國立臺灣海洋大學 === 資訊工程學系 === 97 === In recent years, the enormous utilization of computer and Internet, “network security” has become an extremely important issue. Consequently, the intrusion detection systems are used extensively among this field. In order to defend our network system, we use firewall and intrusion system to protect it. The effects of traditional detecting methods are getting worse in the face of unknown and various network attacks. Therefore, a lot of new detecting methods are proposed with anomaly detection lately to enhance systematic defense capability. We propose a feature selection method to improve the accuracy and detection rate of intrusion detection system. This method chooses specific features using the information of coefficient correlation. In this paper, we use clustering technique and anomaly detection to build the intrusion detection system. The experiments are performed to evaluate detection, classification and false alarm rates. According to the results of our experiments, it proves our new proposed method is better than other traditional intrusion detection approaches.