An unsupervised anomaly detection patterns learning algorithm

被引：0

作者：

Yang, YJ ^{[1
]}

Ma, FY ^{[1
]}

机构：

[1] Shanghai Jiao Tong Univ, Dept Comp Sci & Technol, Shanghai 200030, Peoples R China

来源：

2003 INTERNATIONAL CONFERENCE ON COMMUNICATION TECHNOLOGY, VOL 1 AND 2, PROCEEDINGS | 2003年

关键词：

anomal detection; pattern learning; intrusion detection;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Currently, most anomaly detection patterns learning algorithms require a set of purely normal data from which they train their model. If the data contain some intrusions buried within the training data, the algorithm may not detect these attacks because it will assume that they are normal. In reality, it is very hard to guarantee that there are no attack items in the collected training data. In this paper, we present an unsupervised anomaly detection patterns learning algorithm, which can overcome the shortage.

引用

页码：400 / 402

页数：3

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