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T. Lane and C. E. Brodley, “Temporal Sequence Learning and Data Reduction for Anomaly Detection,” In Proceedings of 5th ACM Conference on Computer & Communication Security, San Francisco, November 3-5, 1998, pp. 150-158.

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.