Skip to main content

Artificial Immune Systems

  • Chapter
  • First Online:

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.

This is a preview of subscription content, log in via an institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   89.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   119.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  • Aickelin U, Cayzer S (2002) The danger theory and its application to artificial immune systems. Research Report HPL-2002-244

    Google Scholar 

  • Al-Hammadi Y, Aickelin U, Greensmith, J (2008) DCA for Bot detection. In: Proceedings of the IEEE WCCI, Hong Kong, pp 1807–1816

    Google Scholar 

  • 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

    Google Scholar 

  • 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

    Google Scholar 

  • Cuppens F et al (2002) Correlation in an intrusion process. In: SECI 2002, TUNIS, TUNISIA

    Google Scholar 

  • Dasgupta, D (ed) (1999) Artificial immune systems and their applications. Springer, Berlin

    Google Scholar 

  • Dasgupta D, Gonzalez F (2002) An immunity-based technique to characterize intrusions in computer networks. IEEE Trans Evol Comput 6:1081–1088

    Google Scholar 

  • De Castro LN, Van Zuben FJ (1999) Artificial Immune Systems: Part 1 – basic theory and applications, Technical Report 1

    Google Scholar 

  • De Castro L N, Von Zuben FJ (2001) aiNet: An Artificial Immune Network for Data Analysis, pp. 231–259. Idea Group Publishing

    Google Scholar 

  • De Castro LN, Von Zuben FJ (2002) Learning and optimization using the clonal selection principle. EEE Trans Evol Comput 6:239–251

    Article  Google Scholar 

  • Esponda F, Forrest S, Helman P (2004) A formal framework for positive and negative detection. IEEE Trans Syst Man Cybern 34:357–373

    Article  Google Scholar 

  • Farmer JD, Packard NH, Perelson AS (1986) The immune system, adaptation, and machine learning. Physica 22:187–204

    Google Scholar 

  • 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

    Google Scholar 

  • Goldsby R, Kindt T, Osborne B (2006) Kuby Immunology: International Edition, 6th edition, W. H. Freeman, San Francisco

    Google Scholar 

  • Greensmith J (2007) The dendritic cell algorithm. PhD thesis, University of Nottingham

    Google Scholar 

  • 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

    Google Scholar 

  • 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

    Google Scholar 

  • Hart E, Timmis J (2008) Application areas of AIS: the past, the present and the future. Appl Soft Comput 8:191–201

    Article  Google Scholar 

  • 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

    Google Scholar 

  • 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

    Google Scholar 

  • Jerne NK (1973) Towards a network theory of the immune system. Ann Immunol 125:373–389

    Google Scholar 

  • 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

    Google Scholar 

  • 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

    Google Scholar 

  • 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

    Article  Google Scholar 

  • Kindt T, Osborne B, Goldsby R (2006) Kuby immunology: international, 6th edn. W. H. Freeman, San Francisco

    Google Scholar 

  • Lay N, Bate I (2008) Improving the reliability of real-time embedded systems using innate immune techniques. Evol Intell 1:113–132

    Article  Google Scholar 

  • Matzinger P (1994) Tolerance, danger and the extended family. Ann Rev Immunol 12:991–1045

    Article  Google Scholar 

  • Matzinger P (2001) The danger model in its historical context. Scand J Immunol 54:4–9

    Article  Google Scholar 

  • Matzinger P (2002) The danger model: a renewed sense of self, Science 296:301–305

    Article  Google Scholar 

  • 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

    Google Scholar 

  • 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

    Google Scholar 

  • Perelson AS, Weisbuch G (1997) Immunology for physicists. Rev Mod Phys 69:1219–1267

    Article  Google Scholar 

  • Resnick P, Varian HR (1997) Recommender systems. Commun ACM 40:56–58

    Article  Google Scholar 

  • Valdes A, Skinner K (2001) Probabilistic alert correlation. In: Proceedings of the RAID 2001, Davis, pp 54–68

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Uwe Aickelin .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer Science+Business Media New York

About this chapter

Cite this chapter

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

Download citation

Publish with us

Policies and ethics