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ICARIS
2005
Springer

A Comparative Study of Real-Valued Negative Selection to Statistical Anomaly Detection Techniques

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A Comparative Study of Real-Valued Negative Selection to Statistical Anomaly Detection Techniques
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 realvalued 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 t...
Thomas Stibor, Jonathan Timmis, Claudia Eckert
Added 27 Jun 2010
Updated 27 Jun 2010
Type Conference
Year 2005
Where ICARIS
Authors Thomas Stibor, Jonathan Timmis, Claudia Eckert
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