Negative Selection Algorithm: A Survey on the Epistemology of Generating Detectors

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.

[1]  Zhou Ji,et al.  Real-Valued Negative Selection Algorithm with Variable-Sized Detectors , 2004, GECCO.

[2]  Senhua Yu,et al.  Exploration of sense of self and humoral immunity for artificial immune systems: algorithms and applications , 2010 .

[3]  Leandro Nunes de Castro,et al.  An Overview of Artificial Immune Systems , 2004 .

[4]  Xiang Zhang,et al.  Vector computer 757 , 1986, Journal of Computer Science and Technology.

[5]  Yang Dongyong,et al.  A study of detector generation algorithms based on artificial immune in intrusion detection system , 2011, 2011 3rd International Conference on Computer Research and Development.

[6]  Hugues Bersini,et al.  Hints for Adaptive Problem Solving Gleaned from Immune Networks , 1990, PPSN.

[7]  Fabio A. González,et al.  An immunity-based technique to characterize intrusions in computer networks , 2002, IEEE Trans. Evol. Comput..

[8]  Slawomir T. Wierzchon,et al.  Discriminative power of the receptors activated by k-contiguous bits rule , 2000 .

[9]  Azzedine Boukerche,et al.  An artificial immune based intrusion detection model for computer and telecommunication systems , 2004, Parallel Comput..

[10]  Hugues Bersini,et al.  The Immune Learning Mechanisms: Recruitment Reinforcement and their applications , 1993 .

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

[12]  Chung-Ming Ou,et al.  Multiagent-based computer virus detection systems: abstraction from dendritic cell algorithm with danger theory , 2013, Telecommun. Syst..

[13]  A. Perelson,et al.  Predicting the size of the T-cell receptor and antibody combining region from consideration of efficient self-nonself discrimination. , 1993, Proceedings of the National Academy of Sciences of the United States of America.

[14]  Arthur M. Silverstein Paul Ehrlich, archives and the history of immunology , 2005, Nature Immunology.

[15]  Jonathan Timmis,et al.  Artificial Immune Systems: A New Computational Intelligence Approach , 2003 .

[16]  Leandro Nunes de Castro,et al.  Artificial Immune Systems: A Novel Approach to Pattern Recognition , 2002 .

[17]  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.

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

[19]  F. Glover,et al.  Handbook of Metaheuristics , 2019, International Series in Operations Research & Management Science.

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

[21]  D. Dasgupta Artificial Immune Systems and Their Applications , 1998, Springer Berlin Heidelberg.

[22]  Lois Boggess,et al.  Non-Euclidean distance measures in AIRS, an artificial immune classification system , 2004, Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753).

[23]  Fabio A. González,et al.  A Randomized Real-Valued Negative Selection Algorithm , 2003, ICARIS.

[24]  Fernando Niño,et al.  A Framework for Evolving Multi-Shaped Detectors in Negative Selection , 2007, 2007 IEEE Symposium on Foundations of Computational Intelligence.

[25]  Jerne Nk Towards a network theory of the immune system. , 1974 .

[26]  Gregg H. Gunsch,et al.  An artificial immune system architecture for computer security applications , 2002, IEEE Trans. Evol. Comput..

[27]  G. Nossal Life, death and the immune system. , 1993, Scientific American.

[28]  John H. Holland,et al.  Induction: Processes of Inference, Learning, and Discovery , 1987, IEEE Expert.

[29]  Jiawei Han,et al.  Data Mining: Concepts and Techniques , 2000 .

[30]  Fernando Niño,et al.  Recent Advances in Artificial Immune Systems: Models and Applications , 2011, Appl. Soft Comput..

[31]  C. Janeway How the immune system recognizes invaders. , 1993, Scientific American.

[32]  Paul Helman,et al.  An immunological approach to change detection: algorithms, analysis and implications , 1996, Proceedings 1996 IEEE Symposium on Security and Privacy.

[33]  Stephanie Forrest,et al.  Coverage and Generalization in an Artificial Immune System , 2002, GECCO.

[34]  Wanli Ma,et al.  A practical study on shape space and its occupancy in negative selection , 2010, IEEE Congress on Evolutionary Computation.

[35]  Claudia Eckert,et al.  The Link between r-contiguous Detectors and k-CNF Satisfiability , 2006, 2006 IEEE International Conference on Evolutionary Computation.

[36]  Rogério de Lemos,et al.  Negative Selection: How to Generate Detectors , 2002 .