Browse > Article
http://dx.doi.org/10.5391/IJFIS.2006.6.2.173

The network model for Detection Systems based on data mining and the false errors  

Lee Se-Yul (Department of Computer Science, Chungwoon University)
Kim Yong-Soo (Department of Computer Engineering, Daejeon University)
Publication Information
International Journal of Fuzzy Logic and Intelligent Systems / v.6, no.2, 2006 , pp. 173-177 More about this Journal
Abstract
This paper investigates the asymmetric costs of false errors to enhance the detection systems performance. The proposed method utilizes the network model to consider the cost ratio of false errors. By comparing false positive errors with false negative errors this scheme achieved better performance on the view point of both security and system performance objectives. The results of our empirical experiment show that the network model provides high accuracy in detection. In addition, the simulation results show that effectiveness of probe detection is enhanced by considering the costs of false errors.
Keywords
Detection Systems; False Errors; Data Mining; Session Patterns;
Citations & Related Records
연도 인용수 순위
  • Reference
1 Lee, W., Stolfo, S. J., 'A data mining framework for building intrusion detection models,' IEEE Symposium on Security and Privacy, pp. 209-220, 1999
2 Safavi-Naini, R., Balachadran, B., 'Case-based reasoning for intrusion detection,' 12th Annual Computer Security Application Conference, pp. 214-223, 1996
3 Denning, D. E., 'An intrusion detection model,' IEEE Trans. S. E., SE-13(2), pp. 222-232, 1987   DOI   ScienceOn
4 Helman, P., 'Statistical foundations of audit trail analysis for the detection of computer misuse,' IEEE Transactions on software engineering, Vol. 19, pp. 861-901, 1993
5 Lee, S. Y., An Adaptive probe detection model using fuzzy cognitive maps, Ph. D. Dissertation, Daejeon University, 2003
6 Vaccaro, H. S., 'Detection of anomalous computer session activity,' Proceedings of the IEEE Symposium on Research in Security and Privacy, pp. 280-289, 1989
7 Richards, K., 'Network based intrusion detection: a review of technologies,' Computer and Security, pp. 671-682, 1999
8 Debar, H., Becker, M., 'A neural network component for an intrusion detection system,' IEEE Computer Society Symposium Research in Security and Privacy, pp. 240-250, 1992
9 Ilgun, K., Kemmerer, R. A., 'Ustat: a real time intrusion system for UNIX,' Proceedings of the IEEE Symposium on Research in Security and Privacy, pp. 16-28, 1993
10 Hubbards, B., Haley, T., McAuliffe, L., Schaefer, L., Kelem, N., Walcott, D., Feiertag, R., Schaefer, M., 'Computer system intrusion detection,' Computer Networks, pp. 120-128, 1990
11 Maxion, R. A., 'Masquerade detection truncated command lines,' International Conference on Dependable Systems and Networks, pp. 219-228, 2002
12 Jasper, R. J., Huang, M. Y., 'A large scale distributed intrusion detection framework based on attack strategy analysis,' Computer Networks, Vol. 31, pp. 2465-2475, 1999   DOI   ScienceOn
13 Lee, S. Y. and Kim, Y. S., 'Design and analysis of probe detection systems for TCP networks,' International Journal of Advanced Computational Intelligence & Intelligent Informatics, Vol. 8, pp. 369-372, 2004   DOI
14 Weber, R., 'Information Systems Control and Audit,' IEEE Symposium on Security and Privacy, pp. 120-128, 1999
15 Debar, H., Dacier, M., 'Towards a taxonomy of intrusion detection systems,' Computer Networks, pp. 805-822, 1989
16 Lippmann, R. P., 'Improving intrusion detection performance using keyword selection and neural networks,' Computer Networks, Vol. 24, pp. 597-603, 2000