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Zanero, S. and Savaresi, S.M. (2004) Unsupervised Learning Techniques for an Intrusion Detection System. Proceedings of the 2004 ACM Symposium on Applied Computing, Nicosia, 14-17 March 2004.

has been cited by the following article:

• TITLE: False Positive Responses Optimization for Intrusion Detection System

  AUTHORS: Jalal Baayer, Boubker Regragui, Aziz Baayer

  KEYWORDS: Cost Model; Intrusion Detection System; False Positive; False Negative; Damage Costs and Optimization

  JOURNAL NAME: Journal of Information Security, Vol.5 No.2, February 20, 2014

  ABSTRACT: In Intrusion Detection Systems (IDS), the operation costs represent one of the big challenges for researchers. They are apart from the IDS cost acquisition and they comprise the costs of maintenance, administration, response, running and errors reactions costs. In the present paper, we focus on the missed reactions which include False Positive (FP) and False Negative (FN) reactions. For that a new optimization cost model is proposed for IDS. This optimization proposes a minimal interval where the IDSs work optimally. In simulation, we found this interval as a trade-off between the damage costs and the FP.