Anomaly Detection for DDoS Attacks via Behavior Profiles Deviation Degree

摘要：

　　Distributed Denial-of-Service (DDoS) attacks present a very serious threat to the stability of the Internet.In this paper,an anomaly detection method for DDoS attacks via Behavior Profiles Deviation Degree (BPDD) is proposed.First,the behavior profiles of normal traffic and real-time traffic are constructed using Markov Chain respectively,and then BPDD is designed to measure the discrepancy of the two profiles.Furthermore,TCM-KNN (Transductive Confidence Machines for K-Nearest Neighbors) algorithm is applied to identify attacks by classifying the BPDD samples.The experimental results demonstrate that the proposed method can effectively distinguish normal traffic from DDoS attacks,and has higher detection ratio and lower false alarm ratio than traditional methods.

关键词： anomaly detection behavior profile TCM-KNN algorithm

作者: Yun Liu Jianping Yin Zhiping Cai Jiarun Lin

作者单位: School of Computer, National University of Defense Technology, Changsha, China

会议类型: 国际会议

会议名称: 2012 2nd international Conference on Materials Science and Information Technology(2012第二届材料科学与信息技术国际会议)(MSIT2012)

会议地点: 西安

会议语种:英文

页码: 777-781

在线出版日期: 2012-08-24（万方平台首次上网日期，不代表论文的发表时间）