Abstract
The biological immune system is a robust, complex, adaptive system that defends the body from foreign pathogens. It is able to categorize all cells (or molecules) within the body as self or nonself substances. It does this with the help of a distributed task force that has the intelligence to take action from a local and also a global perspective using its network of chemical messengers for communication. There are two major branches of the immune system. The innate immune system is an unchanging mechanism that detects and destroys certain invading organisms, whilst the adaptive immune system responds to previously unknown foreign cells and builds a response to them that can remain in the body over a long period of time. This remarkable information processing biological system has caught the attention of computer science in recent years.
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References
Aickelin U, Cayzer S (2002) The danger theory and its application to artificial immune systems. Research Report HPL-2002-244
Al-Hammadi Y, Aickelin U, Greensmith, J (2008) DCA for Bot detection. In: Proceedings of the IEEE WCCI, Hong Kong, pp 1807–1816
Amazon (2003) Recommendations. http://www.amazon.com/
Cayzer S, Aickelin U (2002a) A recommender system based on the immune network. In: Proceedings of the CEC 2002, Honolulu, pp 807–813
Cayzer S, Aickelin U (2002b) On the effects of idiotypic interactions for recommendation communities in artificial immune systems. In: Proceedings of the 1st international conference on artificial immune systems, Canterbury, pp 154–160
Cuppens F et al (2002) Correlation in an intrusion process. In: SECI 2002, TUNIS, TUNISIA
Dasgupta, D (ed) (1999) Artificial immune systems and their applications. Springer, Berlin
Dasgupta D, Gonzalez F (2002) An immunity-based technique to characterize intrusions in computer networks. IEEE Trans Evol Comput 6:1081–1088
De Castro LN, Van Zuben FJ (1999) Artificial Immune Systems: Part 1 – basic theory and applications, Technical Report 1
De Castro L N, Von Zuben FJ (2001) aiNet: An Artificial Immune Network for Data Analysis, pp. 231–259. Idea Group Publishing
De Castro LN, Von Zuben FJ (2002) Learning and optimization using the clonal selection principle. EEE Trans Evol Comput 6:239–251
Esponda F, Forrest S, Helman P (2004) A formal framework for positive and negative detection. IEEE Trans Syst Man Cybern 34:357–373
Farmer JD, Packard NH, Perelson AS (1986) The immune system, adaptation, and machine learning. Physica 22:187–204
Forrest S, Perelson, AS, Allen L, Cherukuri R (1994) Self–nonself discrimination in a computer. In: Proceedings of the IEEE symposium on research in security and privacy, Oakland, CA, USA, pp 202–212
Goldsby R, Kindt T, Osborne B (2006) Kuby Immunology: International Edition, 6th edition, W. H. Freeman, San Francisco
Greensmith J (2007) The dendritic cell algorithm. PhD thesis, University of Nottingham
Gu F, Greensmith J, Aickelin U (2008) Further exploration of the dendritic cell algorithm: antigen multiplier and moving windows. In: Proceedings of the ICARIS, Phuket, pp 142–153
Gu F, Greensmith J, Aickelin U (2009) Integrating real-time analysis with the dendritic cell algorithm through segmentation. In: GECCO 2009, Montreal, pp 1203–1210
Hart E, Timmis J (2008) Application areas of AIS: the past, the present and the future. Appl Soft Comput 8:191–201
Hightower RR, Forrest S, Perelson AS (1995) The evolution of emergent organization in immune system gene libraries. In: Proceedings of the 6th Conference on genetic algorithms, Pittsburgh, pp 344–350
Hoagland J, Staniford S (2002) Viewing intrusion detection systems alerts: lessons from snortsnarf. http://www.silicondefense.com/software/snortsnarf
Hofmeyr S, Forrest S (2000) Architecture for an artificial immune system. Evol Comput 7:1289–1296
Jerne NK (1973) Towards a network theory of the immune system. Ann Immunol 125:373–389
Kim J, Bentley P (2001) Evaluating negative selection in an artificial immune systems for network intrusion detection. In: GECCO 2001, San Francisco, pp 1330–1337
Kim J, Bentley P (2002) Towards an artificial immune systems for network intrusion detection: an investigation of dynamic clonal selection. In: The Congress on Evolutionary Computation 2002, Honolulu, pp 1015–1020
Kim J, Bentley P, Aickelin U, Greensmith J, Tedesco G, Twycross J (2007) Immune system approaches to intrusion detection—a review. Nat Comput 6:413–466
Kindt T, Osborne B, Goldsby R (2006) Kuby immunology: international, 6th edn. W. H. Freeman, San Francisco
Lay N, Bate I (2008) Improving the reliability of real-time embedded systems using innate immune techniques. Evol Intell 1:113–132
Matzinger P (1994) Tolerance, danger and the extended family. Ann Rev Immunol 12:991–1045
Matzinger P (2001) The danger model in its historical context. Scand J Immunol 54:4–9
Matzinger P (2002) The danger model: a renewed sense of self, Science 296:301–305
Ning P, Cui Y, Reeves S (2002) Constructing attack scenarios through correlation of intrusion alerts. In: Proceedings of the 9th ACM conference on computer and communications security, Washington, DC, pp 245–254
Oates B, Greensmith J, Aickelin U, Garibaldi J, Kendall G (2007) The application of a dendritic cell algorithm to a robotic classifier. In: Proceedings of the ICARIS, Santos, Brazil, pp 204–215
Perelson AS, Weisbuch G (1997) Immunology for physicists. Rev Mod Phys 69:1219–1267
Resnick P, Varian HR (1997) Recommender systems. Commun ACM 40:56–58
Valdes A, Skinner K (2001) Probabilistic alert correlation. In: Proceedings of the RAID 2001, Davis, pp 54–68
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Aickelin, U., Dasgupta, D., Gu, F. (2014). Artificial Immune Systems. In: Burke, E., Kendall, G. (eds) Search Methodologies. Springer, Boston, MA. https://doi.org/10.1007/978-1-4614-6940-7_7
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