Information fusion in the immune system

Article, Preprint English OPEN
Twycross, Jamie ; Aickelin, Uwe (2010)
  • Publisher: Elsevier
  • Related identifiers: doi: 10.1016/j.inffus.2009.04.008
  • Subject: Computer Science - Artificial Intelligence | Computer Science - Neural and Evolutionary Computing

Biologically-inspired methods such as evolutionary algorithms and neural networks are proving useful in\ud the field of information fusion. Artificial immune systems (AISs) are a biologically-inspired approach which take inspiration from the biological immune system. Interestingly, recent research has shown how AISs which use multi-level information sources as input data can be used to build effective algorithms for realtime computer intrusion detection. This research is based on biological information fusion mechanisms used by the human immune system and as such might be of interest to the information\ud fusion community. The aim of this paper is to present a summary of some of the biological information fusion mechanisms seen in the human immune system, and of how these mechanisms have been implemented as AISs.
  • References (39)
    39 references, page 1 of 4

    [1] U. Aickelin, J. Greensmith, J. Twycross, Immune system approaches to intrusion detection - a review, in: Proceedings of the Third International Conference on ArtificialImmune Systems, Catania, Italy, LNCS, vol. 3239, 2004, pp. 316-329.

    [2] B. Alberts, A. Johnson, J. Lewis, M. Raff, K. Roberts, P. Walter, Molecular Biology of the Cell, fourth ed., Garland Science, 2002, <http://www.ncbi.nlm.nih.gov/ books/>.

    [3] J. Allen, A. Christie, W. Fithen, J. McHugh, J. Pickel, E. Stoner, State of the practice of intrusion detection technologies, Technical Report CMU/SEI99-TR028, Software Engineering Institute, Carnegie Mellon University, January 2000.

    [4] A.G. Baxter, P.G. Hodgkin, Activation rules:the two-signal theories of immune activation, Nature Reviewsin Immunology 2 (6) (2002) 439-446.

    [5] D. Dasgupta, Advances in artificial immune systems, IEEE Computational Intelligence Magazine 1 (4) (2006) 40-49.

    [6] D. Dasgupta, Artificial Immune Systems and Their Applications, Springer Verlag, New York, 1999.

    [7] D. Dasgupta, R. Azeem, Artificial Immune Systems: A Bibliography, 2006, Published online at <http://ais.cs.memphis.edu/papers/ais_bibliography.pdf>.

    [8] L.N. de Castro, J. Timmis, Artificial Immune Systems: A New Computational Intelligence Approach, Springer, London, 2002.

    [9] H. Debar, M. Dacier, A. Wespi, A revised taxonomy for intrusion detection systems, Annales des Telecommunications 55 (7-8) (2000) 361- 378.

    [10] H.F. Durrant-Whyte, M. Stevens, E. Nettleton, Data fusion in decentralised sensing networks, in: Proceedings of the Fourth International Conference on Information Fusion,Montreal, Canada, 2001, pp. 302-307.

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