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C. F. Tsai, Y. F. Hsu, C. Y. Lin and W. Y. Lin, “Intrusion Detection by Machine Learning: A Review,” Expert Sys- tems with Applications, Vol. 36, No. 10, 2009, pp. 11994- 12000. doi:10.1016/j.eswa.2009.05.029

has been cited by the following article:

• TITLE: Tanimoto Based Similarity Measure for Intrusion Detection System

  AUTHORS: Alok Sharma, Sunil Pranit Lal

  KEYWORDS: Intrusion Detection, ，kNN Classifier, Similarity Measure, Anomaly Detection, Tanimoto Similarity Measure

  JOURNAL NAME: Journal of Information Security, Vol.2 No.4, October 24, 2011

  ABSTRACT: In this paper we introduced Tanimoto based similarity measure for host-based intrusions using binary feature set for training and classification. The k-nearest neighbor (kNN) classifier has been utilized to classify a given process as either normal or attack. The experimentation is conducted on DARPA-1998 database for intrusion detection and compared with other existing techniques. The introduced similarity measure shows promising results by achieving less false positive rate at 100% detection rate.