Improved Unsupervised Anomaly Detection Algorithm

摘要：

In recent years,the network infrastructure has been improved constantly and the information techniques have been applied broadly.Because the misuse detection and anomaly detection methods both have individual benefits and drawbacks,this paper supports the point that combines these two methods to construct the whole intrusion detection system by data mining technique.In this paper,we focus on the improvenmnt of the anomaly detection module in MINDS(Minnesota Intrusion Detection System).By analysis,we use the method of multidimension outlier point detection and adapt the connection score with dynamic weight to improve the performance of intrusion detection system.The improved unsupervised anomaly detection algorithm,also named IUADA,is non-linear,and reduces both the response time and the false alarm rate.

作者: Na Luo Fuyu Yuan Wanli Zuo Fengling He Zhiguo Zhou

作者单位: College of Computer Science and Technology,Jilin University,Changchun,China 130012; Computer Sicence Changchun Institute of Applied Chemstry,Chinese Academy of Sciences,Changchun,China 130022 College of Computer Science and Technology,Jilin University,Changchun,China 130012 Computer Sicence Department,Northeast Normal University,ChangChun,China 130117

会议类型: 国际会议

会议名称: The Third International Conference on Rough Sets and Knowledge Tevhnology(RSKT 2008)(第三届粗糙集与知识技术国际会议)

会议地点: 成都

会议语种:英文

页码: 532-539

在线出版日期: 2008-05-17（万方平台首次上网日期，不代表论文的发表时间）