Performance analysis of soft computing based Anomaly detectors

International Journal of Network Security

Volumn 7, Issue 3, 2008, Pages 436-447

  Srinivasan, N ^a   Vaidehi, V ^a  

^a ANNA UNIVERSITY   (India)

Author keywords

Anomaly detection; False alarm rate; Fuzzy logic; Genetic algorithm; Neural network; Soft computing

Indexed keywords

ANOMALY DETECTION; ANOMALY DETECTION SYSTEMS; ANOMALY DETECTOR; FALSE ALARM RATE; FALSE POSITIVE AND FALSE NEGATIVES; NORMAL BEHAVIOR; PERFORMANCE ANALYSIS; SOFTCOMPUTING TECHNIQUES;

DETECTORS; FUZZY LOGIC; GENETIC ALGORITHMS; NEURAL NETWORKS; SECURITY OF DATA;

SOFT COMPUTING;

EID: 84870573304     PISSN: 1816353X     EISSN: 18163548     Source Type: Journal    
DOI: None     Document Type: Article

References (31)

1. A. Abraham, and R. Jain, "Soft computing models for network intrusion detection system," Soft Computing in Knowledge Discovery: Methods and Applications, ch.16, Studies in Fuzziness and Soft Computing, Springer-Verlag, May 2004.
2. R. Agrawal, and R. Srikant, "Fast algorithms for mining association rules," Proceedings of The 20th International Conference on Very large Databases, pp. 487-499, Santiago, Chile, Sep. 1994.
3. J. Allen, A. Christie, W. Fithen, J. McHugh, J. Pickel, and E. Stoner, State of the Practice of Intrusion Detection Technologies, Carnegie Mellon Software Engineering Institute, SEI Technical Report, CMU/SEI-99-TR-028, 2000.
4. R. G. Bace, Intrusion Detection, Macmillan Technical Publishing, 2000.
5. B. Balajinath and S.V. Raghavan, "Intrusion detection through learning behavioral model," Computer Communications, vol. 24, no. 12, pp. 1202-1212, July 15, 2001.
6. A. Bivens, M. Embrechts, C. Palagiri, R. Smith, and B. Szymanski, "Network-based intrusion detection using neural networks," Proceedins of Artificial Neural networks In Engineering, vol. 12, pp. 10-13, St. Louis, Missouri, Nov. 2002.
7. S. M. Bridges, and R. B. Vaughn, "Fuzzy Data mining and Genetic algorithms applied to Intrusion Detection," Proceedings of the 23rd National Information Systems Security Conference, Baltimore, MD, Oct. 2000.
8. Y. Chen, A. Abraham, and B. Yang, "Hybrid flexible neural-tree-based intrusion detection systems," International Journal of Intelligent Systems, vol. 22, no. 4, pp. 337-352, Apr. 2007.
9. A. Chittur, Model Generation for Intrusion Detection System using Genetic Algorithms, High School Honors Thesis, Ossining High School, Ossining, NY, Nov. 27, 2001.
10. M. Crosbie, and E. Spafford, "Applying genetic programming to intrusion detection," Proceedings of AAAI Fall Symposium, pp. 1-8, AAAI Press, Nov. 1995.
11. D. B. Fogel, Evolutionary Computation: Toward a New Philosophy of Machine Intelligence, Wiley-IEEE Press, 3rd ed., Dec. 2005.
12. F. German, S. Bridges, and R. Vaughn, "An improved algorithm for fuzzy data mining for intrusion detection," Proceedings of the North American Fuzzy Information Processing Society Conference (NAFIPS 2002), New Orleans, LA, June 27-29, 2002.
13. A. K. Ghosh, A. Schwartzbard, and M. Schatz, "Learning Program Behavior Profiles for Intrusion Detection," USENIX Workshop on Intrusion Detection and Network Monitoring, pp. 51-62, California, USA, 1999.
14. J. Gomez, F. Gonzalez, and D. Dasgupta, "An immuno-fuzzy approach to anomaly detection," Proceedings of the 12th IEEE International Conference on Fuzzy Systems, vol. 2, pp 1219-1224, Memphis, TN, USA, May 2003.
15. F. Karray, and C. W. D. Silva, Soft Computing and Intelligent Systems Design, Theory, Tools and Applications, pp. 560, Addison Wesley, Pearson Education Limited, Essex, England, Sep. 2004.
16. H. G. Kayacik, A. N. Z. Heywood, and M. I. Heywood, "A hierarchical SOM-based intrusion detection system," Engineering Applications of Artificial Intelligence, vol. 20, no. 4, pp. 439-451, June 2007.
17. D. S. Kim, H. N. Nguyen, and J. S. Park, "Genetic algorithm to improve SVM based network intrusion detection system," Proceedings of the 19th International Conference on Advanced Information Networking and Applications, vol. 2, pp. 155-158, 2005.
18. W. Lee, S. J. Stolfo, and K.W. Mok, "A data mining framework for building intrusion detection models," IEEE Security and Privacy Symposium, pp. 120-132, Oakland, Canada, 1999.
19. R. P. Lippmann, et al., "Evaluating intrusion detection systems: The 1998 DARPA off-line intrusion detection evaluation," Proceedings of the DARPA Information Survivability Conference and Exposition, IEEE Computer Society Press, 2000.
20. W. Lu, and I. Traore, "Detecting new forms of network intrusion using genetic programming," IEEE Evolutionary Computing, vol. 3. pp. 2165-2172, Dec. 8-12, 2003.
21. M. V. Mahoney, and P. K. Chan, "An analysis of the 1999 DARPA/Lincoln laboratory evaluation data for network anomaly detection," Proceedings of Recent Advances in Intrusion Detection (RAID), pp. 220-237, 2003.
22. S. Mukkamala, and A. H. Sung, Soft Computing Techniques for Intrusion Detection, Workshop on Statistical and Machine Learning Techniques in Computer Intrusion Detection, George Mason University, ONR and AFSOR Sponsored Workshop on Intrusion Detection, Fair Fax, 2003.
23. T. Ozyer, R. Alhajj, and K. Barker, "Intrusion detection by integrating boosting genetic fuzzy classifier and data mining criteria for rule pre-screening," Journal of Network and Computer Applications, vol. 30, no. 1, pp. 99-113, Jan. 2007.
24. M. M. Pillai, J. H. P. Eloff, and H. S. Venter, "An approach to implement network Intrusion detection using Genetic algorithm," Proceedings of the annual South African Institute of Computer Scientists and Information Technologists conference (SAIC- SIT), pp. 221-228, Stellenbosch, SA, Unisa Press, 1- 58113-982-9, Oct. 2004.
25. J. Ryan, M. J. Lin, and R. Miikkulainen, "Intrusion detection with neural networks," Advances in Neural Information Processing Systems, vol. 10, pp. 943-949, The MIT Press, Denver, CO, 1998.
26. S. T. Sarasamma, Q. A. Zhu, and J Huff, "Hierarchical kohonenen net for anomaly detection in network security," IEEE Transactions on Systems, Man and Cybernetics, Part B, vol. 35, no. 2, pp. 302-312, Apr. 2005.
27. N. Srinivasan, and V. Vaidehi, "Anomaly detection in a distributed environment using neural networks on a cluster," IASTED International Conference on Communication, Network, and Information Security (CNIS 2005), Phoenix, USA, Nov. 2005.
28. C. H. Tsang, S. Kwong, and H. Wang, "Genetic- fuzzy rule mining approach and evaluation of feature selection techniques for anomaly intrusion detection," Pattern Recognition, vol. 40, no. 9, pp. 2373- 2391, Sep. 2007.
29. W.Wang, and S. M. Bridges, "Genetic algorithm optimization of membership functions for mining fuzzy association rules," Proceedings of the 7th International Conference on Fuzzy Theory & Technology, pp.131-134, Atlantic City, NJ, Feb. 27-Mar. 3, 2000.
30. M. Wang, C. Zhang, and J. Yu, "Native API based windows anomaly intrusion detection method using SVM," Proceedings of IEEE International Conference on Sensor Networks, Ubiquitous, and Trustworthy Computing, vol. 1, pp. 514- 5191, Xi'an Jiaotong University, China, June 05-07, 2006.
31. L. A. Zadeh, "Outline of a new approach to the analysis of complex systems and decision processes," IEEE Transactions on Systems, Man, and Cybernetics, vol. 3, pp. 28-44, 1973.