Anomaly Detection Using Higher-Order Feature

摘要：

Learning-based anomaly detection method is often subject to inaccuracies due to noise, small sample size, bad choice of parameter for the estimator, etc. We propose a novel method using higher-order feature, based on the sequence nonparametric test to assess the reliability of the estimation. The method allows an expert to discover informative features for separation of normal and attack instances. We performed experiments on the KDD Cup dataset The results show that method reveals the nature of attacks. Application of the method yields a major improvement of detection accuracy.

关键词： anomafy detection KDD Cup dataset sequence nonparametric test mutual information

作者: Xiang CHENG Yuan-Chun XU Yi-Lai ZHANG Bing-Xiang LIU

作者单位: Information engineering Institute, Jingdezhen Ceramic Institute Jindezhen,China

会议类型: 国际会议

会议名称: Third International Conference on Information and Computing(第三届信息与计算科学国际会议 ICIC 2010)

会议地点: 无锡

会议语种:英文

页码: 131-134

在线出版日期: 2010-06-04（万方平台首次上网日期，不代表论文的发表时间）