Is negative selection appropriate for anomaly detection?

• DOI: 10.1145/1068009.1068061
• Publication Date: 2005-08-03T08:31:47Z
• Authors: Thomas Stibor; Philipp Mohr; Jonathan Timmis; Claudia Eckert
• Keywords: 0202 electrical engineering, electronic engineering, information engineering; 02 engineering and technology

Negative selection algorithms for hamming and real-valued shape-spaces are reviewed. Problems are identified with the use of these shape-spaces, and the negative selection algorithm in general, when applied to anomaly detection. A straightforward self detector classification principle is proposed and its classification performance is compared to a real-valued negative selection algorithm and to a one-class support vector machine. Earlier work suggests that real-value negative selection requires a single class to learn from. The investigations presented in this paper reveal, however, that when applied to anomaly detection, the real-valued negative selection and self detector classification techniques require positive and negative examples to achieve a high classification accuracy. Whereas, one-class SVMs only require examples from a single class.