An Online Adaptive Network Anomaly Detection Model

摘要：

Proposed a novel online adaptive network anomaly detection model (OANAD). Purely normal dataset is not needed for training. It can process the network traffic data stream in real-time, alert the abnormal traffic, and dynamically build up its local normal pattern base and intrusion pattern base. The model has a relatively simple architecture which makes it efficient for processing online network traffic data. Also the detecting algorithms cost little computational time. The experiment on the KDD 99 intrusion detection datasets shows that our model achieves a detection rate of 90.51％ and a false positive rate of only 0.19％ within a very short running time.

作者: Xiaotao Wei Houkuan Huang Shengfeng Tian Xiaohui Yang Baomin Xu

作者单位: Beijing Jiaotong University, Beijing, China

会议类型: 国际会议

会议名称: The Second International Joint Conference on Computational Science and Optimization(CSO 2009)(2009 国际计算科学与优化会议)

会议地点: 三亚

会议语种:英文

页码: 1415-1418

在线出版日期: 2009-04-24（万方平台首次上网日期，不代表论文的发表时间）