Abstract
Within the Artificial Immune System community, the most widely implemented algorithm is the Negative Selection Algorithm. Its performance rest solely on the interaction between the detector generation algorithm and matching technique adopted for use. Relying on the type of data representation, either for strings or real-valued, the proper detection algorithm must be assigned. Thus, the detectors are allowed to efficaciously cover the non-self space with small number of detectors. In this paper, the di_erent categories of detection generation algorithm and matching rule have been presented. Briey, the biologial and arti_- cial immune system, as well as the theory of negative selection algorithm were introduced. The exhaustive detector generation algorithm used in the original Negative Selection Algorithm laid the foundation at proferring other algorithmic methods based on set of rules in generating valid detectors for revealing anomalies.
Access provided by Autonomous University of Puebla. Download to read the full chapter text
Chapter PDF
Similar content being viewed by others
References
Forrest, S., Perelson, A.S., Allen, L., Cherukuri, R.: Self-nonself discrimination in a computer. In: Research in Security and Privacy, 1994. Proceedings., 1994 IEEE Computer Society Symposium on, IEEE (1994) 202–212
Silverstein, A.M.: Paul ehrlich, archives and the history of immunology. Nature immunology 6(7) (2005) 639–639
Immune, A.: Artificial immune systems. (2006) 107–118
Greensmith, J., Whitbrook, A., Aickelin, U.: Artificial immune systems. In: Handbook of Metaheuristics. Springer (2010) 421–448
Aickelin, U., Dasgupta, D.: Artificial immune systems. In: Search Methodologies. Springer (2005) 375–399 9
Boukerche, A., Jucá, K.R.L., Sobral, J.B., Notare, M.S.M.A.: An artificial immune based intrusion detection model for computer and telecommunication systems. Parallel Computing 30(5) (2004) 629–646
Janeway Jr, C.A.: How the immune system recognizes invaders. life, death and the immune system. Scientific American 269(3) (1993) 72
Ou, C.M.: Multiagent-based computer virus detection systems: abstraction from dendritic cell algorithm with danger theory. Telecommunication Systems (2011) 1–11
Dasgupta, D.: An overview of artificial immune systems. In Dasgupta, D (Ed.), Artificial Immune Systems and Their Applications (1998) 3–19
Dasgupta, D., Yu, S., Nino, F.: Recent advances in artificial immune systems: models and applications. Applied Soft Computing 11(2) (2011) 1574–1587
De Castro, L.N., Timmis, J.: Artificial immune systems: a novel approach to pattern recognition. (2002) 67–84
de Castro, L.N., Timmis, J.: Artificial immune systems: a new computational intelligence approach. Springer Verlag (2002)
De Castro, L.N., Von Zuben, F.J.: Artificial immune systems: Part i—basic theory and applications. Technical Report—RT DCA 01/99, School of Computing and Electrical Enginnering. State University of Campinas, Brazil (1999)
Bersini, H., Varela, F.J.: Hints for adaptive problem solving gleaned from immune networks. In: Parallel problem solving from nature. Springer (1991) 343–354
Bersini, H., Varela, F.: The immune learning mechanisms: reinforcement, recruitment and their applications. Computing with Biological Metaphors 1(2) (1994) 166–192
Stibor, T., Timmis, J., Eckert, C.: The link between r-contiguous detectors and k-cnf satisfiability. In: Evolutionary Computation, 2006. CEC 2006. IEEE Congress on, IEEE (2006) 491–498
Ji, Z., Dasgupta, D.: Revisiting negative selection algorithms. Evolutionary Computation 15(2) (2007) 223–251
Majd, Mahshid, A.H., Hashemi, S.: A polymorphic convex hull scheme for negative selection algorithms. International Journal of Innovative Computing, Information and Control 8(5A) (2012) 2953–2964
Ji, Z., Dasgupta, D.: Real-valued negative selection algorithm with variable-sized detectors. In: Genetic and Evolutionary Computation—GECCO 2004, Springer (2004) 287–298
Percus, J.K., Percus, O.E., Perelson, A.S.: Predicting the size of the t-cell receptor and antibody combining region from consideration of eficient self-nonself discrimination. Proceedings of the National Academy of Sciences 90(5) (1993) 1691–1695
Holland, J.H., Holyoak, K.J., Nisbett, R.E., Thagard, P.R.: Induction: Processes of inference, learning, and discovery. computational models of cognition and perception (1986)
Balthrop, J., Esponda, F., Forrest, S., Glickman, M.: Coverage and generalization in an artificial immune system. In: Proceedings of the Genetic and Evolutionary Computation Conference, Citeseer (2002) 3–10
Jerne, N.K.: Towards the network theory of the immune system. Ann. Immunol.(Inst. Pasteur) 125C (1974) 373–389
Harmer, P.K., Williams, P.D., Gunsch, G.H., Lamont, G.B.: An artificial immune system architecture for computer security applications. Evolutionary computation, IEEE transactions on 6(3) (2002) 252–280 10
Chen, J., Yang, D., Naofumi, M.: A study of detector generation algorithms based on artificial immune in intrusion detection system. WSEAS TRANSACTIONS on BIOLOGY and BIOMEDICINE 4(3) (2007) 29–35
Han, J., Kamber, M., Pei, J.: Data Mining: Concepts and Techniques. Morgan Kaufmann (2011)
Dasgupta, D., KrishnaKumar, K., Wong, D., Berry, M.: Negative selection algorithm for aircraft fault detection. In: Artificial Immune Systems. Springer (2004) 1–13
Hamaker, J.S., Boggess, L.: Non-euclidean distance measures in airs, an artificial immune classification system. In: Evolutionary Computation, 2004. CEC2004. Congress on. Volume 1., IEEE (2004) 1067–1073
D’Haeseleer, P., Forrest, S., et al.: An immunological approach to change detection. In: Proc. of IEEE Symposium on Research in Security and Privacy, Oakland, CA. (1996)
Ayara, M., Timmis, J., de Lemos, R., de Castro, L.N., Duncan, R.: Negative selection: How to generate detectors. In: Proceedings of the 1st International Conference on Artificial Immune Systems (ICARIS). Volume 1., Canterbury, UK:[sn] (2002) 89–98
D’Haeseleer, P., Forrest, S., Helman, P.: An immunological approach to change detection: Algorithms, analysis and implications. In: Security and Privacy, 1996. Proceedings., 1996 IEEE Symposium on, IEEE (1996) 110–119
Wierzchon, S.T.: Discriminative power of the receptors activated by k-contiguous bits rule. Journal of Computer Science & Technology 1(3) (2000) 1–13
Yu, S., Adviser-Dasgupta, D.: Exploration of sense of self and humoral immunity for artificial immune systems: algorithms and applications. 361
Dasgupta, D., González, F.: An immunity-based technique to characterize intrusions in computer networks. Evolutionary Computation, IEEE Transactions on 6(3) (2002) 281–291
González, F.A., Dasgupta, D.: Anomaly detection using real-valued negative selection. Genetic Programming and Evolvable Machines 4(4) (2003) 383–403
Balachandran, S., Dasgupta, D., Nino, F., Garrett, D.: A framework for evolving multi-shaped detectors in negative selection. In: Foundations of Computational Intelligence, 2007. FOCI 2007. IEEE Symposium on, IEEE (2007) 401–408
Gonzalez, F., Dasgupta, D., Niño, L.F.: A randomized real-valued negative selection algorithm. In: Artificial Immune Systems. Springer (2003) 261–272
Ma, W., Tran, D., Sharma, D.: A practical study on shape space and its occupancy in negative selection. In: Evolutionary Computation (CEC), 2010 IEEE Congress on, IEEE (2010) 1–7
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer Science+Business Media Singapore
About this paper
Cite this paper
Lasisi, A., Ghazali, R., Herawan, T. (2014). Negative Selection Algorithm: A Survey on the Epistemology of Generating Detectors. In: Herawan, T., Deris, M., Abawajy, J. (eds) Proceedings of the First International Conference on Advanced Data and Information Engineering (DaEng-2013). Lecture Notes in Electrical Engineering, vol 285. Springer, Singapore. https://doi.org/10.1007/978-981-4585-18-7_20
Download citation
DOI: https://doi.org/10.1007/978-981-4585-18-7_20
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-4585-17-0
Online ISBN: 978-981-4585-18-7
eBook Packages: EngineeringEngineering (R0)