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Twycross, Jamie; Aickelin, Uwe (2010)
Publisher: Elsevier
Languages: English
Types: Article
Subjects: 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.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • [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, .
    • [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 .
    • [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.
    • [11] A.M. Gallegos, M.J. Bevan, Central tolerance to tissue-specific antigens mediated by direct and indirect antigen presentation, Journal of Experimental Medicine 200 (8) (2004) 1039-1049.
    • [12] D. Gao, M.K. Reiter, D. Song, Gray-box extraction of execution graphs for anomaly detection, in: Proceedings of the ACM Conference on Computer and Communications Security, Washington, DC, 2004, pp. 318-329.
    • [13] V. Gorodetsky, O. Karsaev, I. Kotenko, V. Samoilov, Multi-agent information fusion: methodology, architecture and software tools for learning of object and situation assessment, in: Proceedings of the Ninth International Conference on Information Fusion, Florence, Italy, 2006, pp. 346-353.
    • [14] J. Greensmith, U. Aickelin, J. Twycross, Articulation and clarification of the dendritic cell algorithm, in: Proceedings of the Fifth International Conference on Artificial Immune Systems, Oeiras, Portugal, LNCS, vol. 4163, 2006, pp. 404- 417.
    • [15] J. Greensmith, J. Twycross, U. Aickelin, Dendritic cells for anomaly detection, in: Proceedings of the IEEE World Congress on Computational Intelligence, Vancouver, Canada, 2006, pp. 664-671.
    • [16] D.L. Hall, J. Llinas (Eds.), Handbook of Multisensor Data Fusion, CRC Press LLC, Boca Raton, Florida, 2001.
    • [17] E. Hart, J. Timmis,Application areas of AIS: the past, the present and the future, in: Proceedings of the Fourth International Conference on Artificial Immune Systems, Banff, Canada, LNCS, vol. 3627, 2005, pp. 483-497.
    • [18] C.A. Janeway, The road less traveled: the role of innate immunity in the adaptive immune response - presidential address to the American Association of Immunologists, Journal of Immunology 161 (2) (1998) 539- 544.
    • [19] C.A. Janeway, R. Medzhitov, Innate immune recognition, Annual Review of Immunology 20 (1) (2002) 197-216.
    • [20] C.A. Janeway, P. Travers, M. Walport, M. Shlomchik, Immunobiology: The Immune System in Health and Disease, sixth ed., Garland Publishing, 2005, .
    • [21] R. Kemmerer, G. Vigna, Intrusion detection: a brief history and overview, Security and Privacy, Supplement to IEEE Computer Magazine 35 (4) (2002) 27-30.
    • [22] B. Kyewski, J. Derbinski, Self-representation in the thymus: an extended view, Nature Reviews in Immunology 4 (9) (2004) 688-698.
    • [23] libtissue sourcecode and datasets, 2007, .
    • [24] M. Liggins, C.Y. Chong, I. Kadar, M.G. Alford, V. Vannicola, S. Thomopoulos, Distributed fusion architectures and algorithms for target tracking,Proceedingsof the IEEE 85 (1) (1997) 95-107.
    • [25] H. Lodish, A. Berk, P. Matsudaira, C.A. Kaiser, M. Krieger, M.P. Scott, L. Zipursky, J. Darnell, Molecular Cell Biology, fourth ed., W.H. Freeman and Co., 1999, .
    • [26] R.C. Luo, A.M.D. Shr, C.Y. Hu, Multiagent based multisensor resource management, in: Proceedings of the IEEE International Conference on Intelligent Robotics and Systems, Victoria, Canada, vol. 2, 1998, pp. 1034- 1039.
    • [27] I.V. Maslov, I. Gertner, Multi-sensor fusion: an evolutionary algorithm approach, Information Fusion 7 (3) (2006) 304-330.
    • [28] R. Medzhitov, C.A. Janeway, How does the immune system distinguish self from nonself?, Seminars in Immunology 12 (3) (2000) 185-188
    • [29] R. Medzhitov, C.A. Janeway, Innate immunity, The New England Journal of Medicine 343 (5) (2000) 338-344.
    • [30] Proceedings of the International Conference on Artificial Immune Systems, 2002-2007, .
    • [31] C. Reis e Sousa, Toll-like receptors and dendritic cells: for whom the bug tolls, Seminars in Immunology 16 (1) (2004) 27-34.
    • [32] L. Segel, I.R. Cohen (Eds.), Design Principles for the Immune Systemand Other Distributed Autonomous Systems, Oxford University Press, New York, 2001.
    • [33] J. Timmis, Artificial immune systems - today and tomorrow, Natural Computing 6 (1) (2007) 1-18.
    • [34] J. Twycross, Integrated innate and adaptive artificial immune systems applied to process anomaly detection, PhD Thesis, School of Computer Science, University of Nottingham, UK, 2007.
    • [35] J. Twycross, U. Aickelin, libtissue - implementing innate immunity, in: Proceedings of the IEEE World Congress on Computational Intelligence, Vancouver, Canada, July 2006, pp. 499-506.
    • [36] J. Twycross, U. Aickelin, An immune-inspired approach to anomaly detection, in: J.N.D. Gupta, S.K. Sharma (Eds.), Handbook of Research on Information Assurance and Security,IGI Global, New York, 2009, pp. 109-121 (Chapter 10).
    • [37] A. Watkins, J. Timmis, Exploiting parallelism inherent in AIRS, an artificial immune classifier, in: Proceedings of the Third International Conference on Artificial Immune Systems, Catania, Italy, LNCS, vol. 3239, 2004, pp. 427-438.
    • [38] N. Xiong, P. Svensson, Multi-sensor management for informationfusion:issues and approaches, Information Fusion 3 (2) (2002) 163--186.
    • [39] H. Xu, W. Du, S.J. Chapin, Context sensitive anomaly monitoring of process control flow to detect mimicry attacks and impossible paths, in: Proceedings of the Seventh International Symposium on Recent Advances in Intrusion Detection, Sophia Antipolis, France, LNCS, vol. 3224, 2004, pp. 21-38.
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