Negative Selection as a Means of Discovering Unknown Temporal Patterns
The temporal nature of negative selection is an under exploited area. In a negative selection system, newly generated antibodies go through a maturing phase, and the survivors of the phase then wait to be activated by the incoming antigens after certain number of matches. These without having enough matches will age and die, while these with enough matches (i.e., being activated) will become active detectors. A currently active detector may also age and die if it cannot find any match in a pre-defined (lengthy) period of time. Therefore, what matters in a negative selection system is the dynamics of the involved parties in the current time window, not the whole time duration, which may be up to eternity. This property has the potential to define the uniqueness of negative selection in comparison with the other approaches. On the other hand, a negative selection system is only trained with “normal" data samples. It has to learn and discover unknown “abnormal" data patterns on the fly by itself. Consequently, it is more appreciate to utilize negation selection as a system for pattern discovery and recognition rather than just pattern recognition. In this paper, we study the potential of using negative selection in discovering unknown temporal patterns.
[1] Hofmeyr, S.A. and S. Forrest. Immunity by Design: An Artificial Immune
System. in Proceedings of the Genetic and Evolutionary Computation
Conference (GECCO 1999). 1999. Orlando, Florida, USA: Morgan
Kaufmann.
[2] Dasgupta, D., Z. Ji, and F. Gonzalez. Artificial immune system (AIS)
research in the last five years. in The 2003 Congress on Evolutionary
Computation (CEC-03). 2003: IEEE Press.
[3] Castro, L.N.D. and J. Timmis, Artificial Immune Systems: A New
Computational Intelligence Approach. 2002: Springer.
[4] Hofmeyr, S., An Immunology Model of Distributed Detection and Its
Application to Computer Security, in Department of Computer Science.
1999, University of New Mexico, USA.
[5] Forrest, S., A.S. Perelson, L. Allen, and R. Cherukuri. Self-Nonself
Discrimination in a Computer. in Proceedings of the 1994 IEEE
Symposium on Research in Security and Privacy. 1994. Oakland, CA,
USA: IEEE Computer Society Press.
[6] Hofmeyr, S.A., S. Forrest, and A. Somayaji, Intrusion Detection Using
Sequences of System Calls. Journal of Computer Security, 1998. 6: p.
151-180.
[7] Hofmeyr, S.A. and S. Forrest, Architecture for an Artificial Immune
System. Evolutionary Computation, 2000. 8(4): p. 443-473.
[8] Balthrop, J., S. Forrest, and M.R. Glickman. Revisiting LISYS:
Parameters and normal behavior. in Proceedings of the Congress on
Evolutionary Computing (CEC-2002). 2002.
[9] Gabrielli, N. and M. Rigodanzo. An Artificial Immune System for
Network Intrusion. Detection on a Web Server: First Results. in
Proceedings of the 2nd Italian Workshop on Evolutionary Computation
(GSICE 2006). 2006.
[10] Gonzalez, F.A. and D. Dasgupta, Anomaly Detection Using Real-Valued
Negative Selection. Genetic Programming and Evolvable Machines,
2003. 4(4): p. 383-403.
[11] Ji, Z. and D. Dasgupta, Revisiting Negative Selection Algorithms.
Evolutionary Computation, 2007. 15(2): p. 223-251.
[12] Dasgupta, D., K. Kumar, D. Wong, and M. Berry. Negative Selection
Algorithm for Aircraft Fault Detection. in Proceedings of the Third
International Conference on Artificial Immune Systems (ICARIS 2004).
2004
[13] Hart, E. and J. Timmis. Application Areas of AIS: The Past, The Present
and The Future. in Proceedings of Artificial Immune Systems: 4th
International Conference, ICARIS 2005. 2005. Banff, Alberta, Canada:
Springer.
[14] Timmis, J., Artificial immune systems - today and tomorrow. Natural
Computing: an international journal, 2007. 6(1): p. 1-18.
[15] Garrett, S.M., How Do We Evaluate Artificial Immune Systems?
Evolutionary Computation, 2005. 13(2): p. 145 - 177.
[16] Dasgupta, D., Advances in artificial immune systems. IEEE
Computational Intelligence Magazine, 2006. 1(4): p. 40 - 49.
[17] Keogh, E, General Time Series Tutorial. (cited 20 December 2009);
Available from:
http://www.cs.ucr.edu/~eamonn/Keogh_Time_Series_CDrom.zip.
[18] Ma, W., D. Tran, and D. Sharma. Negative Selection with Antigen
Feedback in Intrusion Detection. in 7th International Conference on
Artificial Immune Systems (ICARIS 2008).
[19] Cormack, G. and T. Lynam. 2007 TREC Public Spam Corpus,
http://plg.uwaterloo.ca/~gvcormac/treccorpus07/. 2007 (cited 15
Janurary 2009).
[20] Prakash, V.V. Digest-Nilsimsa, http://search.cpan.org/dist/Digest-
Nilsimsa/. 2002 (cited 20 February 2009).
[21] Damiani, E., S.D.C.d. Vimercati, S. Paraboschi, and P.S. Damiani. An
Open Digest-based Technique for Spam Detection. in Proc. of the 2004
International Workshop on Security in Parallel and Distributed Systems.
2004.
[22] Ma, W., D. Tran, and D. Sharma. A Novel Spam Email Detection
System Based on Negative Selection, in 4th ICCIT: 2009 International
Conference on Computer Sciences and Convergence Information
Technology. 2009.
[1] Hofmeyr, S.A. and S. Forrest. Immunity by Design: An Artificial Immune
System. in Proceedings of the Genetic and Evolutionary Computation
Conference (GECCO 1999). 1999. Orlando, Florida, USA: Morgan
Kaufmann.
[2] Dasgupta, D., Z. Ji, and F. Gonzalez. Artificial immune system (AIS)
research in the last five years. in The 2003 Congress on Evolutionary
Computation (CEC-03). 2003: IEEE Press.
[3] Castro, L.N.D. and J. Timmis, Artificial Immune Systems: A New
Computational Intelligence Approach. 2002: Springer.
[4] Hofmeyr, S., An Immunology Model of Distributed Detection and Its
Application to Computer Security, in Department of Computer Science.
1999, University of New Mexico, USA.
[5] Forrest, S., A.S. Perelson, L. Allen, and R. Cherukuri. Self-Nonself
Discrimination in a Computer. in Proceedings of the 1994 IEEE
Symposium on Research in Security and Privacy. 1994. Oakland, CA,
USA: IEEE Computer Society Press.
[6] Hofmeyr, S.A., S. Forrest, and A. Somayaji, Intrusion Detection Using
Sequences of System Calls. Journal of Computer Security, 1998. 6: p.
151-180.
[7] Hofmeyr, S.A. and S. Forrest, Architecture for an Artificial Immune
System. Evolutionary Computation, 2000. 8(4): p. 443-473.
[8] Balthrop, J., S. Forrest, and M.R. Glickman. Revisiting LISYS:
Parameters and normal behavior. in Proceedings of the Congress on
Evolutionary Computing (CEC-2002). 2002.
[9] Gabrielli, N. and M. Rigodanzo. An Artificial Immune System for
Network Intrusion. Detection on a Web Server: First Results. in
Proceedings of the 2nd Italian Workshop on Evolutionary Computation
(GSICE 2006). 2006.
[10] Gonzalez, F.A. and D. Dasgupta, Anomaly Detection Using Real-Valued
Negative Selection. Genetic Programming and Evolvable Machines,
2003. 4(4): p. 383-403.
[11] Ji, Z. and D. Dasgupta, Revisiting Negative Selection Algorithms.
Evolutionary Computation, 2007. 15(2): p. 223-251.
[12] Dasgupta, D., K. Kumar, D. Wong, and M. Berry. Negative Selection
Algorithm for Aircraft Fault Detection. in Proceedings of the Third
International Conference on Artificial Immune Systems (ICARIS 2004).
2004
[13] Hart, E. and J. Timmis. Application Areas of AIS: The Past, The Present
and The Future. in Proceedings of Artificial Immune Systems: 4th
International Conference, ICARIS 2005. 2005. Banff, Alberta, Canada:
Springer.
[14] Timmis, J., Artificial immune systems - today and tomorrow. Natural
Computing: an international journal, 2007. 6(1): p. 1-18.
[15] Garrett, S.M., How Do We Evaluate Artificial Immune Systems?
Evolutionary Computation, 2005. 13(2): p. 145 - 177.
[16] Dasgupta, D., Advances in artificial immune systems. IEEE
Computational Intelligence Magazine, 2006. 1(4): p. 40 - 49.
[17] Keogh, E, General Time Series Tutorial. (cited 20 December 2009);
Available from:
http://www.cs.ucr.edu/~eamonn/Keogh_Time_Series_CDrom.zip.
[18] Ma, W., D. Tran, and D. Sharma. Negative Selection with Antigen
Feedback in Intrusion Detection. in 7th International Conference on
Artificial Immune Systems (ICARIS 2008).
[19] Cormack, G. and T. Lynam. 2007 TREC Public Spam Corpus,
http://plg.uwaterloo.ca/~gvcormac/treccorpus07/. 2007 (cited 15
Janurary 2009).
[20] Prakash, V.V. Digest-Nilsimsa, http://search.cpan.org/dist/Digest-
Nilsimsa/. 2002 (cited 20 February 2009).
[21] Damiani, E., S.D.C.d. Vimercati, S. Paraboschi, and P.S. Damiani. An
Open Digest-based Technique for Spam Detection. in Proc. of the 2004
International Workshop on Security in Parallel and Distributed Systems.
2004.
[22] Ma, W., D. Tran, and D. Sharma. A Novel Spam Email Detection
System Based on Negative Selection, in 4th ICCIT: 2009 International
Conference on Computer Sciences and Convergence Information
Technology. 2009.
@article{"International Journal of Medical, Medicine and Health Sciences:51732", author = "Wanli Ma and Dat Tran and Dharmendra Sharma", title = "Negative Selection as a Means of Discovering Unknown Temporal Patterns", abstract = "The temporal nature of negative selection is an under exploited area. In a negative selection system, newly generated antibodies go through a maturing phase, and the survivors of the phase then wait to be activated by the incoming antigens after certain number of matches. These without having enough matches will age and die, while these with enough matches (i.e., being activated) will become active detectors. A currently active detector may also age and die if it cannot find any match in a pre-defined (lengthy) period of time. Therefore, what matters in a negative selection system is the dynamics of the involved parties in the current time window, not the whole time duration, which may be up to eternity. This property has the potential to define the uniqueness of negative selection in comparison with the other approaches. On the other hand, a negative selection system is only trained with “normal" data samples. It has to learn and discover unknown “abnormal" data patterns on the fly by itself. Consequently, it is more appreciate to utilize negation selection as a system for pattern discovery and recognition rather than just pattern recognition. In this paper, we study the potential of using negative selection in discovering unknown temporal patterns.
", keywords = "Artificial Immune Systems, ComputationalIntelligence, Negative Selection, Pattern Discovery.", volume = "4", number = "5", pages = "170-7", }
