Clustering Toward Detecting Cyber Attacks

摘要：

Several anomaly methods have been proposed to cope with the recent booming of HTTP-related vulnerabilities which renders the security breaches of lots of vital HTTP-based services on the internet. This paper proposes a novel bottom-up agglomerative clustering method which not only spares the nuisance of a learning process that involves a big amount of manual sample taggings, but also presents a much stronger adaptiveness in being able to coping with variant situations and in detecting new samples.

关键词： agglomerative clustering intrusion detection HTTP attacks data minning

作者: Xiaofeng Yang Wei Li Mingming Sun Xuelei Hu Shuqin Li Yongzhi Li

作者单位: School of Computer Science and Technology Nanjing University of Science and Technology Nanjing, Chin Department of Information and Computer Science Nanjing Forestry University Nanjing, China

会议类型: 国际会议

会议名称: The 2010 International Conference on Computer Application and System Modeling(2010计算机应用与系统建模国际会议 ICCASM 2010)

会议地点: 太原

会议语种:英文

页码: 243-247

在线出版日期: 2010-10-22（万方平台首次上网日期，不代表论文的发表时间）