MILA — Multilevel Immune Learning Algorithm
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The biological immune system is an intricate network of specialized tissues, organs, cells, and chemical molecules. T-cell-dependent humoral immune response is one of the complex immunological events, involving interaction of B cells with antigens (Ag) and their proliferation, differentiation and subsequent secretion of antibodies (Ab). Inspired by these immunological principles, we proposed a Multilevel Immune Learning Algorithm (MILA) for novel pattern recognition. It incorporates multiple detection schema, clonal expansion and dynamic detector generation mechanisms in a single framework. Different test problems are studied and experimented with MILA for performance evaluation. Preliminary results show that MILA is flexible and efficient in detecting anomalies and novelties in data patterns.
KeywordsDetection Rate False Alarm Rate Anomaly Detection Artificial Immune System Recognition Phase
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- 1.Dasgupta, D., Attoh-Okine, N.: Immunity-Based Systems: A Survey. In the proceedings of the IEEE International Conference on Systems, Man and Cybernetics, Orlando, October 12–15, 1997Google Scholar
- 3.Forrest, S., Somayaji, A., Ackley, D.: Building Diverse Computer Systems. Proc. of the Sixth Workshop on Hot Topics in Operating Systems (1997).Google Scholar
- 4.Forrest, S., Perelson, A. S., Allen, L., Cherukuri, R.: Self-nonself discrimination in a computer. Proc. of the IEEE Symposium on Research in Security and Privacy, IEEE Computer Society Press, Los Alamitos, CA, (1994) 202–212Google Scholar
- 5.Dasgupta, D., Forrest, S.: An Anomaly Detection Algorithm Inspired by the Immune System. In: Dasgupta D (eds) Artificial Immune Systems and Their Applications, Springer-Verlag, (1999) 262–277Google Scholar
- 8.Keogh, E., Folias, T.: The UCR Time Series Data Mining Archive [http://www.cs.ucr.edu/~eamonn/TSDMA/index.html]. Riverside CA. University of California — Computer Science & Engineering Department. (2002)
- 9.D’haeseleer, P., Forrest, S., Helman, P.: An immunological approach to change detection: algorithms, analysis, and implications. Proceedings of the 1996 IEEE Symposium on Computer Security and Privacy, IEEE Computer Society Press, Los Alamitos, CA, (1996) 110–119Google Scholar
- 11.Gonzalez, F., Dasgupta, D.: Neuro-Immune and SOM-Based Approaches: A Comparison. Proceedings of 1st International Conference on Artificial Immune Systems (ICARIS-2002), University of Kent at Canterbury, UK, September 9th–11th, 2002Google Scholar