文章基本信息

标题：Efficient Statistics based Framework for Network Intrusion Detection
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作者：Kuo-Chen Lee ; Zhi-Jun Hsu ; Li Liu 等
期刊名称：International Journal of Soft Computing & Engineering
电子版ISSN：2231-2307
出版年度：2013
卷号：3
期号：1
页码：399-408
出版社：International Journal of Soft Computing & Engineering
摘要：Due to the growing threat of network attacks, detecting and measuring network abuse are increasingly important. Network intrusion detection is one of the most frequently deployed approaches. Most detection systems only rely on signature matching methods and, therefore, they suffer from novel attacks. This investigation presents a simple yet efficient data-mining framework (SID) that constructs a statistics based abusive traffic detection system based on network flows. We show that SID can accurately and automatically detect existing and new malicious network attempts. Experimental results validate the feasibility of using SID to detect network anomaly intrusions. In particular, we show that, simply employing four basic features of network flows, SID can yield an accuracy of over 97% with a false positive rate of 0.03% in the testing dataset.
关键词：Data Mining; Intrusion Detection; Network;Security; Machine Learning.