Abstract
Danger Theory is presented with particular predominance on analogies in the Artificial Immune Systems world. Artificial Immune System (AIS) is relatively naive paradigm for intelligent computations. The inspiration for AIS is derived from natural Immune System (IS). The idea is that the artificial cells release signals describing their status, e.g., safe signals and danger signals. The various artificial cells use the signals in order to adapt their behavior. This new theory suggests that the immune system reacts to threats based on the correlation of various (danger) signals and it provides a method of ‘grounding’ the immune response, i.e., linking it directly to the attacker. In this paper, we look at Danger Theory from the perspective of AIS practitioners and an overview of the Danger Theory is presented with particular emphasis on analogies in the Artificial Immune Systems world.
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References
Matzinger P (1998) An Innate sense of danger, Seminars in Immunology, pp 399–415
Matzinger P (2001) The danger model in its historical context. Scand J Immunol 54:4–9
Matzinger P (1994) Tolerance danger and the extended family. Annu Rev Immunol 12:991–1045
Burgess M (1998) Computer Immunology. Proc LISA XII:283–297
Williamson M (2002) Biologically inspired approaches to computer security, HP labs technical reports HPL-2002, pp 131
Somayaji A, Hofmeyr S, Forrest S (1998) Principles of a computer immune system. In: Proceedings New Security Paradigms Workshop, Charlottesville, pp 75–82
Aickelin U, Cayzer S (2002) The danger theory and its application to artificial immune systems. In: Proceedings of the 1st internet conference on artificial immune systems. ICARIS, Springer, pp 141–148
Aickelin U, Bentley P, Cayzer S, Kim J, McLeod J (2003) Danger theory: the link between AIS and IDS? In: Proceedings ICARIS-2003, 2nd international conference on artificial immune systems. ICARIS, Springer, pp 147–155
Goldsby R, Kindt T, Osborne B (2000) Kuby immunology, 4th edn. W H Freeman, New York
Bretscher P, Cohn M (1970) A theory of self–nonself discrimination. Science 169:1042–1049
Matzinger P (1994) Tolerance, danger and the extended family. Annu Rev Immunol 12:991–1045
Sundaram A (1996) An introduction to intrusion detection, crossroads: the ACM student magazine 2(4)
Forrest S, Perelson A, Allen L, Cherukuri R (1994) Self non-self discrimination in a computer. In: Proceedings of the 1994 IEEE symposium on research in security and privacy, pp 202–212
Forrest S, Hofmeyr S, Somayaji A, Longstaff T (1996) A sense of self for unix processes. In: Proceedings of the 1996 IEEE symposium on research in security and privacy, pp 120–128
Hofmeyr S, Forrest S (2000) Architecture for an artificial immune system. Evolutionary Computation 8(4):443–473
Somayaji A, Forrest S (2000) Automated response using system-call delays. In: Proceedings of the ninth USENIX security symposium, pp 185–197
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We would like to thank the two anonymous reviewers, whose comments greatly improved this paper.
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Kumar, S., Saurabh, P., Verma, B. (2013). Biologically Motivated Approaches for Complex Problem Solving. In: Kumar, V., Bhatele, M. (eds) Proceedings of All India Seminar on Biomedical Engineering 2012 (AISOBE 2012). Lecture Notes in Bioengineering. Springer, India. https://doi.org/10.1007/978-81-322-0970-6_23
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DOI: https://doi.org/10.1007/978-81-322-0970-6_23
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