Anomaly Intrusion Detection for Evolving Data Stream Based on Semi-supervised Learning

作者：Yu Yan^*; Guo Shanqing; Lan Shaohua; Ban Tao

来源：15th International Conference on Neuro-Information Processing, 2008-11-25 to 2008-11-28.

In network environment, time-varying traffic patterns make the detection model not characterize the current traffic accurately. At the same time, the deficiency of training samples also degrades the detection accuracy. This paper proposes an anomaly detection algorithm for evolving data stream based on semi-supervised learning. The algorithm uses data stream model with attenuation to solve the problem of the change of traffic patterns, as while as extended labeled dataset generated from semi-supervised learning is used to train detection model. The experimental results manifest that the algorithm have better accuracy than those based on all historical data equivalently by forgetting historical data gracefully, as while as be suitable for the situation of deficiency of labeled data.

• 出版日期2009
• 单位南京大学