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. Keywords—Artificial Immune Systems, Computational Intelligence, Negative Selection, Pattern Discovery.

[1]  Stephanie Forrest,et al.  Intrusion Detection Using Sequences of System Calls , 1998, J. Comput. Secur..

[2]  Jonathan Timmis,et al.  Artificial immune systems—today and tomorrow , 2007, Natural Computing.

[3]  D. Dasgupta,et al.  Advances in artificial immune systems , 2006, IEEE Computational Intelligence Magazine.

[4]  Alan S. Perelson,et al.  Self-nonself discrimination in a computer , 1994, Proceedings of 1994 IEEE Computer Society Symposium on Research in Security and Privacy.

[5]  Stephanie Forrest,et al.  Revisiting LISYS: parameters and normal behavior , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[6]  Stephanie Forrest,et al.  Architecture for an Artificial Immune System , 2000, Evolutionary Computation.

[7]  Zhou Ji,et al.  Artificial immune system (AIS) research in the last five years , 2003, The 2003 Congress on Evolutionary Computation, 2003. CEC '03..

[8]  Jonathan Timmis,et al.  Application Areas of AIS: The Past, The Present and The Future , 2005, ICARIS.

[9]  Simon M. Garrett,et al.  How Do We Evaluate Artificial Immune Systems? , 2005, Evolutionary Computation.

[10]  Fabio A. González,et al.  Anomaly Detection Using Real-Valued Negative Selection , 2003, Genetic Programming and Evolvable Machines.

[11]  D. Wong,et al.  Negative Selection Algorithm for Aircraft Fault Detection , 2004, ICARIS.

[12]  Nicoletta Gabrielli,et al.  An Artificial Immune System for Network Intrusion Detection on a Web Server : First Results , .

[13]  F. Azuaje.  Artificial Immune Systems: A New Computational Intelligence Approach , 2003 .

[14]  Wanli Ma,et al.  Negative Selection with Antigen Feedback in Intrusion Detection , 2008, ICARIS.

[15]  Ernesto Damiani,et al.  An Open Digest-based Technique for Spam Detection , 2004, PDCS.

[16]  Stephanie Forrest,et al.  Immunity by design: an artificial immune system , 1999 .

[17]  Wanli Ma,et al.  A Novel Spam Email Detection System Based on Negative Selection , 2009, 2009 Fourth International Conference on Computer Sciences and Convergence Information Technology.

[18]  Zhou Ji,et al.  Revisiting Negative Selection Algorithms , 2007, Evolutionary Computation.