An immuno-fuzzy approach to anomaly detection

IEEE International Conference on Fuzzy Systems

Volumn 2, Issue , 2003, Pages 1219-1224

  Gómez, Jonatan ^a   González, Fabio ^a   Dasgupta, Dipankar ^a  

^a NONE

Author keywords

[No Author keywords available]

Indexed keywords

ERROR DETECTION; EVOLUTIONARY ALGORITHMS; GENETIC ALGORITHMS; NEURAL NETWORKS; PROBLEM SOLVING; SECURITY OF DATA;

ANOMALY DETECTION;

FUZZY CONTROL;

EID: 0038537231     PISSN: 10987584     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (30)

1. D. Dasgupta and S. Forrest, "Novelty detection in time series data using ideas from immunology," in Proceedings of the International Conference on Intelligent Systems, pp. 82-87, June 1996.
2. E. Keogh, S. Lonardi, and B. Chiu, "Finding surprising patterns in a time series database in linear time and space," in Proceedings of the eighth acm sigkdd international conference on knowledge discovery and data mining (kdd '02), (Alberta, Canada), 2002.
3. T. Yoshikiyo, "Fault detection by mining association rules from house-keeping data," in proceedings of international symposium on artificial intelligence, robotics and automation in space (i-sairas 2001), (Montreal, Canada), June 2001.
4. D. Denning, "An intrusion-detection model," in Ieee computer society symposium on research in security and privacy, pp. 118-31, 1986.
5. T. Lane, Machine learning techniques for the computer security. PhD thesis, Purdue University, 2000
6. W. Lee and S. Stolfo, "Data mining approaches for intrusion detection," in Proceedings of the 7th USENIX security symposium, (San Antonio, TX), 1998.
7. D. Dagupta and F. González, "An immunity-based technique to characterize intrusions in computer networks," IEEE Transactions on Evolutionary Computation, vol. 6, pp. 281-291, June 2002.
8. F. Gonzalez and D. Dasgupta, "An imunogenetic technique to detect anomalies in network traffic," in Gecco 2002: proceedings of the genetic and evolutionary computation conference, (New York), pp. 1081-1088, Morgan Kaufmann Publishers, 9-13 July 2002.
9. S. Hofmeyr and S. Forrest, "Architecture for an artificial immune system," Evolutionary Computation, vol. 8, no. 4, pp. 443-473, 2000.
10. D. Dasgupta, Artificial immune systems and their applications. New York: Springer-Verlag, 1999.
11. J. Kephart, "A biologically inspired immune system for computers," in Proceedings of Artificial Life, (Cambridge, MA), pp. 130-139, July 1994.
12. J. Kim and P. Bentley, "An evaluation of negative selection in an artificial immune system for network intrusion detection," in Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2001), (San Francisco, California, USA), pp. 1330-1337, Morgan Kaufmann, 2001.
13. S. Forrest, A. Perelson, L. Allen, and R. Cherukuri, "Self-nonself discrimination in a computer," in Proc. IEEE Symp. on Research in Security and Privacy, pp. 202-212, 1994.
14. D. Dasgupta, "An overview of artificial immune systems and their applications.," in Artificial immune systems and their applications (D. Dasgupta, ed.), pp. pp 3-23, Springer-Verlag, Inc., 1999.
15. D. Dasgupta and S. Forrest, "An anomaly detection algorithm inspired by the immune system," in Artificial immune systems and their applications, pp. 262-277, Springer-Verlag, Inc., 1999.
16. D. Dasgupta and S. Forrest, "Tool breakage detection in milling operations using a negative-selection algorithm," Technical Report CS95-5, Department of Computer Science, University of New Mexico, 1995.
17. A. Tyrrell, "Computer know thy self!: A biological way to look at fault tolerance," in 2nd euromicro/ieee workshop on dependable computing systems, (Milan), pp. 129-135, 1999.
18. F. González, D. Dasgupta, and R. Kozma, "Combining negative selection and classificationcation techniques for anomaly detection," in Proceedings of the congress on evolutionary computation CEC2002, (Honolulu, HI), pp. 705-710, IEEE, May 2002.
19. C. A. C. Coello and N. C. Cortes, "A parallel implementation of the artificial immune system to handle constraints in genetic algorithms: preliminary results," in Proceedings of the 2002 Congress on Evolutionary Computation CEC2002, (Honolulu, Hawaii), pp. 819-824, 2002.
20. P. Simpson, "Fuzzy min-max neural networks. I. Classification," IEEE Transactions on Neural Network, vol. 3, pp. 776-786, sep 1992.
21. P. Simpson, "Fuzzy min-max neural networks. II. clustering," IEEE Transactions on Fuzzy Systems, vol. 1, pp. 32-, feb 1993.
22. D. Fogel and P. Simpson, "Experiments with evolving fuzzy clusters," in Proceedings of the Second Annual Conference on Evolutionary Programming (D. Fogel and W. Atmar, eds.), (La Jolla, California), pp. 90-97, 1993.
23. A. Ghozeil and D. B. Fogel, "Discovering patterns in spatial data using evolutionary programming," in Genetic Programming 1996: Proceedings of the First Annual Conference, (Stanford University, CA, USA), pp. 521-527, MIT Press, 28-31 July 1996.
24. S. W. Mahfoud, "Crowding and preselection revisited," in Parallel problem solving from nature 2 (R. Männer and B. Manderick, eds.), (Amsterdam), pp. 27-36, North-Holland, 1992.
25. D. Beasley, D. Bull, and R. Martin, "A sequential niche technique for multimodal function optimization," Evolutionary Computation, vol. 1, no. 2, pp. 101-125, 1993.
26. F. Provost, T. Fawcett, and R. Kohavi, "The case against accuracy estimation for comparing induction algorithms," in Proceedings of 15th international conference on machine learning, (San Francisco, Ca), pp. 445-453, Morgan Kaufmann, 1998.
27. T. Caudell and D. Newman, "An adaptive resonance architecture to define normality and detect novelties in time series and databases," (Portland, Oregon), pp. 166-176, 1993.
28. "Mit lincoln labs. 1999 darpa intrusion detection evaluation." In http://www.ll.mit.edu/IST/ideval/index.html, 1999.
29. J. Gomez and D. Dasgupta, "Evolving Fuzzy Classifiers for Intrusion Detection," in Proceedings of the 2002 IEEE Workshop on Information Assurance, June 2002.
30. W. Fan, W. Lee, M. Miller, S. Stolfo, and P. Chan, "Using artificial anomalies to detect unknown and known network intrusions," in Proceedings of the first IEEE International conference on Data Mining, 2001.