Is negative selection appropriate for anomaly detection ?

GECCO 2005 - Genetic and Evolutionary Computation Conference

Volumn , Issue , 2005, Pages 321-328

  Stibor, Thomas ^a   Mohr, Philipp ^b   Timmis, Jonathan ^b  

^a DARMSTADT UNIVERSITY OF TECHNOLOGY   (Germany)

^b UNIVERSITY OF KENT   (United Kingdom)

Author keywords

Anomaly Detection; Artificial Immune Systems; Negative Selection; One Class SVM; Positive Selection

Indexed keywords

ANOMALY DETECTION; ARTIFICIAL IMMUNE SYSTEMS; NEGATIVE SELECTION ALGORITHMS; SELF DETECTOR CLASSIFICATION PRINCIPLE;

ALGORITHMS; VECTORS;

GENETIC ENGINEERING;

EID: 32444436687     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1068009.1068061     Document Type: Conference Paper

References (28)

1. M. Ayara, J. Timmis, L. de Lemos, R. de Castro, and R. Duncan. Negative selection: How to generate detectors. In 1st Int. Conf. on Artificial Immune Systems, pages 89-98, September 2002.
2. J. Balthrop, F. Esponda, S. Forrest, and M. Glickman. Coverage and generalization in an artificial immune system. In GECCO 2002: Proc. of the Genetic and Evolutionary Computation Conf., pages 3-10, New York, 9-13 July 2002. Morgan Kaufmann Publishers.
3. C.-C. Chang and C.-J. Lin. LIBSVM: a Library for Support Vector Machines (http://www.csie.ntu.edu.tw/~cjlin/papers/libsvm.pdf), December 2004.
4. D. Dasgupta, S. Forrest. Novelty Detection in Time Series Data Using Ideas from Immunology, In Proc. of the 5th Int. Conf. on Intelligent Systems. IEEE Computer Society Press, 1996.
5. D. G. Osmond, The turn-over of B-cell populations. Immunology Today, 14(1):34-37, 1993.
6. L. N. de Castro and J. Timmis, Artificial Immune Systems: A New Computational Intelligence Approach. Springer-Verlag, 2002.
7. P. D'haeseleer. An immunological approach to change detection: Theoretical results. In Proc. 9th IEEE Computer Security Foundations Workshop, pages 18-26, 1996.
8. Elad Yom-Tov. An Introduction to Pattern Classification, In Advanced Lectures on Machine Learning, volume 3176 of LNCS, pages 1-20. Springer-Verlag, 2004.
9. F. Esponda, S. Forrest, and P. Helman. A formal framework for positive and negative detection schemes. IEEE Transactions on Systems, Man and Cybernetics Part B: Cybernetics, 34(1):357-373, 2004.
10. T. Fawcett. ROC graphs: Notes and practical considerations for data mining researchers. Technical Report HPL-2003-4, Hewlett Packard Laboratories, Jan. 17 2003.
11. A. Freitas and J. Timmis. Revisiting the Foundations of Artificial Immune Systems: A Problem Oriented Perspective. In Proc. of the 2nd Int. Conf. on Artificial Immune Systems, volume 2787 of LNCS, pages 229-241. Springer, September 2003.
12. F. González, D. Dasgupta, and G. Gomez. The effect of binary matching rules in negative selection. In Genetic and Evolutionary Computation - GECCO-2003, volume 2723 of LNCS, pages 195-206, Chicago, 12-16 July 2003. Springer-Verlag.
13. F. González, D. Dasgupta, and R. Kozma. Combining negative selection and classification techniques for anomaly detection. In Congress on Evolutionary Computation, pages 705-710. IEEE, May 2002.
14. F. A. González, D. Dasgupta, and L. F. Niño. A Randomized Real-Valued Negative Selection Algorithm. In Proc. of the 2nd Int. Conf. on Artificial Immune Systems (ICARIS), volume 2787 of LNCS, pages 261-272, Edinburgh, UK, 2003. Springer-Verlag.
15. S. A. Hofmeyer. An Interpretative Introduction to the Immune System, 2000.
16. C.-W. Hsu, C.-C. Chang, and C.-J. Lin. A Pratical Guide to Support Vector Classification (http://www.csie.ntu.edu.tw/~cjlin/papers/guide/guide.pdf), July 2003.
17. C. A. Janeway, P. Travers, M. Walport, and M. Shlomchik. Immunologie. Spektrum Akademischer Verlag, 2002.
18. Z. Ji and D. Dasgupta. Real-valued negative selection algorithm with variable-sized detectors. In Genetic and Evolutionary Computation - GECCO-2004, Part I, volume 3102 of LNCS, pages 287-298, Seattle, WA, USA, 26-30 June 2004. Springer-Verlag.
19. K.-R. Müller, S. Mika, G. Rätsch, K. Tsuda, and B. Schölkopf. An Introduction to Kernel-Based Learning Algorithms, Transactions on Neural Networks, 12(2):181-201, 2001.
20. J. K. Percus, O. E. Percus, and A. S. Perelson. Predicting the Size of the T-Cell Receptor and Antibody Combining Region from Consideration of Efficient Self-Nonself Discrimination. Proc. of National Academy of Sciences USA, 90:1691-1695, 1993.
21. A. S. Perelson and G. Oster. Theoretical studies of clonal selection: minimal antibody repertoire size and reliability of self-nonself discrimination. In J. Theor. Biol, volume 81, pages 645-670, 1979.
22. S. Forrest, A.S. Perelson, L. Allen, R. Cherukuri, Self-Nonself Discrimination in a Computer. In Proc. of the 1994 IEEE Symposium on Research in Security and Privacy. IEEE Computer Society Press, 1994.
23. B. Schölkopf. Statistical learning and kernel methods. Technical Report MSR-TR-2000-23, Microsoft Research (MSR), Feb, 2000.
24. B. Schölkopf, J. C. Platt, S.-T. Shawe-Taylor, A. J. Smola, and W. Williamson. Estimating the support of a high-dimensional distribution. Technical Report MSR-TR-99-87, Microsoft Research (MSR), Nov. 1999.
25. A. Seeker, A. A. Freitas, and J. Timmis. AISEC: An artificial immune system for e-mail classification. In Proc. of the 2003 Congress on Evolutionary Computation CEC2003, pages 131-138, Canberra, 8-12 Dec. 2003. IEEE Press.
26. T. Stibor, K. M. Bayarou, and C. Eckert. An investigation of R-chunk detector generation on higher alphabets, In Genetic and Evolutionary Computation - GECCO-2004, Part I, volume 3102 of LNCS, pages 299-307, Seattle, WA, USA, 26-30 June 2004. Springer-Verlag.
27. P. Vlachos. StatLib. http://lib.stat.cmu.edu.
28. A. Watkins and L. Boggess. A new classifier based on resource limited artificial immune systems. In Proc. of the 2002 Congress on Evolutionary Computation CEC2002, pages 1546-1551. IEEE Press, 2002.