By the same authors

A comparative study of real-valued negative selection to statistical anomaly detection techniques

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Publication details

Title of host publicationARTIFICIAL IMMUNE SYSTEMS, PROCEEDINGS
DatePublished - 2005
Pages262-275
Number of pages14
PublisherSPRINGER-VERLAG BERLIN
Place of PublicationBERLIN
EditorsC Jacob, ML Pilat, PJ Bentley, J Timmis
Original languageEnglish
ISBN (Print)3-540-28175-4

Abstract

The (randomized) real-valued negative selection algorithm is an anomaly detection approach, inspired by the negative selection immune system principle. The algorithm was proposed to overcome scaling problems inherent in the hamming shape-space negative selection algorithm. In this paper, we investigate termination behavior of the real-valued negative selection algorithm with variable-sized detectors on an artificial data set. We then undertake an analysis and comparison of the classification performance on the high-dimensional KDD data set of the real-valued negative selection, a real-valued positive selection and statistical anomaly detection techniques. Results reveal that in terms of detection rate, real-valued negative selection with variable-sized detectors is not competitive to statistical anomaly detection techniques on the KDD data set. In addition, we suggest that the termination guarantee of the real-valued negative selection with variable-sized detectors is very sensitive to several parameters.

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