Publication: Effectiveness evaluation of data mining based IDS
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2006-07
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Springer
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Abstract
Data mining has been widely applied to the problem of Intrusion Detection in computer networks. However, the misconception of the underlying problem has led to out of context results. This paper shows that factors such as the probability of intrusion and the costs of responding to detected intrusions must be taken into account in order to compare the effectiveness of machine learning algorithms over the intrusion detection domain. Furthermore, we show the advantages of combining different detection techniques. Results regarding the well known 1999 KDD dataset are shown.
Description
Proceeding of: 6th Industrial Conference on Data Mining, ICDM 2006, Leipzig, Germany, July 14-15, 2006.
Keywords
Data mining, Intrusion detection, IDS, Effectiveness
Bibliographic citation
Advances in data mining, applications in medicine, web mining, marketing, image and signal mining: 6th Industrial Conference on Data Mining, ICDM 2006. Springer, 2006, pp. 377-388