Published in International Journal of Advanced Research in Computer Science Engineering and Information Technology
ISSN: 2321-3337 Impact Factor:1.521 Volume:4 Issue:2 Year: 21 February,2015 Pages:375-385
Intrusion detection is a process of monitoring the various computer networks and systems for violations of security and this can be automatically done with the help of an intrusion detection system. An Intrusion Detection System (IDS) is a critical component for secure information management. IDS play a major role in detecting and disrupting various attacks before cooperating with the software. This work presents the investigations carried out on Echo State Network (ESN) structures for intrusion detection. New algorithms have been presented which have faster convergence and better performance in IDS from a set of available information in the database. This paper has been implemented with the KDD dataset to experiment the performance of ESN in classifying the Local Area Network (LAN) intrusion packets.
Echo State Network, Intrusion Detection, ANN, Malicious, DoS.
1. Baojun Zhang, Xuezeng Pan, and Jiebing Wang, “Hybrid Intrusion Detection System for Complicated Network”, Fourth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2007), 2007. 2. Gang Kou, Yi Peng, Yong Shi, and Zhengxin Chen, “Network Intrusion Detection by Multi-group Mathematical Programming based Classifier”, Sixth IEEE International Conference on Data Mining - Workshops (ICDMW'06), 2006. 3. Helman P., and Liepins G., “Statistical foundations of audit trail analysis for the detection of computer misuse”, IEEE Transaction on Software Engineering, Vol. 19, Issue 9, pp. 886-901, 1993. 4. Hu Zhengbing, Li Zhitang, and Wu Junqi, “A Novel Network Intrusion Detection System (NIDS) Based on Signatures Search of Data Mining”, Proceedings of the Workshop on Knowledge Discovery and Data Mining, 2008. 5. Jaeger H., “Short term memory in echo state networks”, Tech. Rep. No. 152, Bremen: German National Research Center for Information Technology, 2002. 6. Jaeger H., “The Echo State Approach to Analysing and Training Recurrent Neural Networks”, Technical Report- GMD Report 148, German National Research Center for Information Technology, 2001. 7. Jaeger H., “Tutorial on training recurrent neural networks, covering BPPT, RTRL, EKF and the echo state network approach”, GMD Report 159, Fraunhofer Institute AIS, 2002. 8. Jingg-Sheng Xue, Ji-Zhou Sun, and Xu Zhang, “Recurrent Network in Network in Network Intrusion Detection System”, Proceedings of the 3rd International Conference on Machine Learning and Cybernetics, Shanghai, 26-29 August, 2006. 9. John Zhong Lei, and Ali Ghorbani, “Network Intrusion Detection using an improved competitive learning neural network”, Proceedings of the second annual conference on Communication Networks and Services Research (CNSR ’04), 2004. 10. Katar C., “Combining multiple techniques for intrusion detection”, International Journal on Computer Science and Network Security (IJCSNS), Vol. 6, No. 2B, pp. 208-218, Feb. 2006. 11. Liu J., Yu F., Lung C. H., and Tang H., “Optimal combined intrusion detection and biometric-based continuous authentication in high security mobile ad hoc networks’, IEEE Transactions on Wireless Communication, Vol. 8, No. 2, pp. 806-815, February 2009. 12. Marra S., Iachino M., and Morabito F., “Tanh-like activation function implementation for high-performance digital neural systems”, Research in Microelectronics and Electronics 2006, pp. 237–240, June 2006. 13. Mishra A., Nadkarni K., and Patcha V. T. A., “Intrusion detection in wireless ad hoc networks”, IEEE Wireless Communication, Vol. 11, No. 1, pp. 48-60, Feb. 2004. 14. Moses Garuba, Chunmei Liu, and Duane Fraites, “Intrusion Techniques: Comparative Study of Network Intrusion Detection Systems”, Proceedings of the fifth International Conference on Information Technology: New Generations, 987-0-7695-3099-4/08 $25.00 ©, IEEE 2008. 15. Qing-Hua L, Sheng-Yi Jiang, Xin Li, “A Supervised Intrusion Detection Method”, Proceedings of the 3rd International Conference on Machine Learning and Cybernetics, Shanghai, 26-29 August 2004. 16. Sampada Chavan, Khusbu Shah, Neha Dave, and Sanghamitra Mukherjee, “Adaptive Neuro-Fuzzy Intrusion Detection Systems”, Proceedings of the International Conference on Information Technology: Coding and Computing (ITCC’04), 2004. 17. Shengrong Bu, Richard Yu F., Xiaoping P. Liu, Peter Mason, and Helen Tang, “Distributed Combined Authentication and Intrusion Detection with Data Fusion in High-Security Mobile Ad hoc Networks”, IEEE Transactions on Vehicular Technology, Vol. 60, No. 3, pp. 1025-1036, March 2011