The week's pick
Reseach Article
Rule based Network Intrusion Detection using Genetic Algorithm
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 18 - Number 8 |
| Year of Publication: 2011 |
| Authors: M. Sadiq Ali Khan |
10.5120/2303-2914
|
M. Sadiq Ali Khan . Rule based Network Intrusion Detection using Genetic Algorithm. International Journal of Computer Applications. 18, 8 ( March 2011), 26-29. DOI=10.5120/2303-2914
Abstract
The rapid increase of information technology usage demands the high level of security in order to keep the data resources and equipments of the user secure. In this current era of networks, there is an eventual stipulate for development of consistent, extensible, easily manageable and have low maintenance cost solutions for Intrusion Detection. Network Intrusion Detection based on rules formulation is an efficient approach to classify various type of attack. DoS or Probing attack are relatively more common and can be detected more accurately if contributing parameters are formulated in terms of rules. Genetic Algorithm is used to devise such rule. It is found that accuracy of rule based learning increases with the number of iteration.
References
- McHugh, John, 2001. “Intrusion and Intrusion Detection.” Technical Report. CERT Coordination Center, Software Engineering Institute, Carnegie Mellon University.
- D. Wagner and D. Dean, “Intrusion detection via static analysis,” in Proc. IEEE Symposium on Research in Security and Privacy, Oakland, CA, 2001.
- Miller, Brad. L. and Michael J. Shaw. 1996. “Genetic Algorithms with Dynamic Niche Sharing for Multimodal Function Optimization.” In Proceedings of IEEE International Conf. on Evolutionary Computation, pp. 786-791.Nagoya University, Japan.
- Wan Tang; Yang Cao; Xi-Min Yang and Won-Ho So, “Study on Adaptive Intrusion Detection Engine Based on Gene Expression Programming Rules”, Computer2008, Vol 3; Pages 959-963
- Santhosh Kumar, S.; Vignesh, J.; Rangarajan, L.R.; Narayanan, V.S.; Rangarajan, K.M.; Venkatkrishna, A.L., “A Fast Time Scale Genetic Algorithm based Image Segmentation using Cellular Neural Networks”, Signal Processing and Communications, 2007. ICSPC 2007, IEEE, Pages 908-911.
- N. Ye, X. Li, Q. Chen S. M. Emran, and M. Xu, “Probabilistic techniques for intrusion detection based on computer audit data,” IEEE Trans. SMC-A, vol. 31, pp. 266–274, Jul. 2001.
- Baoyi Wang; Feng Li; Shaomin Zhang, “Research on Intrusion Detection Based on Campus Network”, Intelligent Information Technology Application, 2009 Vol 1, Pages 468-471.
- Akyazi, U; Uyar and A.S.E., “Distributed Intrusion Detection Using Mobile agenets against DDoS attacks”, Computer and Information Sciences 2008, Pages 1-6.
- Fiskiran, A.M; Lee, R.B., “Runtime Execution Monitoring to detect and prevent malicious code execution”, Computer Design: VLSI in computers and Processors 2004, IEEE International Conference Pages 452-457.
- Jiang, M.; Munawar, M.; Reidemeister, T.; Ward, P, “Efficient Fault Detection and Diagnosis in complex Software Systems with Information- Theoretic Monitoring”, IEEE transactions on Dependable and Secure Computing 2011, Issue 99.
- Nicoletta Dessì and Barbara Pes, “An Evolutionary Method for Combining Different Feature Selection Criteria in Microarray Data Classification”, Journal of Artificial Evolution and applications, Vol 2009.
- Burney S. M. Aqil, Sadiq Ali Khan and Jawed Naseem, 2010, “Efficient Probabilistic Classification Methods for NIDS”, (IJCSIS) International Journal of Computer Science and Information Security, Vol. 8, No. pp168-172
Index Terms
Keywords