Improved Extreme Learning Machine (IELM) Classifier For Intrusion Detection System

  IJETT-book-cover  International Journal of Engineering Trends and Technology (IJETT)          
© 2016 by IJETT Journal
Volume-41 Number-2
Year of Publication : 2016
Authors : R.Karthik, Dr.S.Veni, Dr.B.L.Shivakumar
DOI :  10.14445/22315381/IJETT-V41P212


R.Karthik, Dr.S.Veni, Dr.B.L.Shivakumar"Improved Extreme Learning Machine (IELM) Classifier For Intrusion Detection System", International Journal of Engineering Trends and Technology (IJETT), V41(2),66-71 November 2016. ISSN:2231-5381. published by seventh sense research group

This research work aims in design and development of an improved extreme learning machine classifier for intrusion detection system. The proposed research work contributed a single layer neural network which is trained starting with hidden nodes to the maximum number of hidden nodes and the expected learning accuracy. The improved ELM makes use of an intermediate variable in the overall recursive process which obtains better learning rate with reduced error. KDD cup’99 dataset that contains four major types of attacks in the network is chosen for performing IELM classification. Performance metrics detection rate and false alarm rate are chosen. Simulation results shows that the proposed IELM classifier outperforms in terms of improved detection rate and reduced false alarm rate.


[1] Overview of Attack Trends, attack_trends.pdf, 2002.
[2] K.K. Gupta, B. Nath, R. Kotagiri, and A. Kazi, “Attacking Confidentiality: An Agent Based Approach,” Proc. IEEE Int’l Conf. Intelligence and Security Informatics (ISI ’06), vol. 3975, 2006, pp. 285-296.
[3] J Anderson, An Introduction to Neural Networks (MIT, Cambridge, 1995)
[4] B Rhodes, J Mahaffey, J Cannady, Multiple self-organizing maps for intrusion detection, Paper presented at the Proceedings of the 23rd National Information Systems Security Conference, Baltimore,2000, pp 16–19.
[5] A. Sung, S. Mukkamala, Identifying important features for intrusion detection using support vector machines and neural networks in Symposium on Applications and the Internet, 2003, pp. 209–216.
[6] R.Karthik, and B.L Shivakumar, “A Taxonomy of Network Intrusion Detection System for Wireless Communication”, International Journal of Computer Science And Engineering, vol. 3, December 2015, pp. 35-42, issue-12, E- ISSN: 2347-2693.
[7] R.Karthik, S.Veni and B.L Shivakumar, “Fuzzy Based Support Vector Machine Classifier With Wiener Filter (Fsvm – Wf) For Intrusion Detection System”, International Journal of Advanced Research in Computer Science , vol. 7, July – August 2016, pp. 11-15, issue-4, E- ISSN: 0976-5697.
[8] N.B. Amor, S. Benferhat, and Z. Elouedi, “Naive Bayes vs. Decision Trees in Intrusion Detection Systems,” Proc. ACM Symp. Applied Computing (SAC ’04), 2000, pp. 420-424.
[9] H. Debar, M. Becke, and D. Siboni, “A Neural Network Component for an Intrusion Detection System,” Proc. IEEE Symp. Research in Security and Privacy (RSP ’92), 1992, pp. 240-250.
[10] Z. Zhang, J. Li, C.N. Manikopoulos, J. Jorgenson, and J. Ucles, “HIDE: A Hierarchical Network Intrusion Detection System Using Statistical Preprocessing and Neural Network Classification,” Proc. IEEE Workshop Information Assurance and Security (IAW ’01), 2001, pp. 85-90.
[11] D.S. Kim and J.S. Park, “Network-Based Intrusion Detection with Support Vector Machines,” Proc. Information Networking, NetworkingTechnologies for Enhanced Internet Services Int’l Conf. (ICOIN ’03), 2003, pp. 747-756.
[12] Y.-S. Wu, B. Foo, Y. Mei, and S. Bagchi, “Collaborative Intrusion Detection System (CIDS): A Framework for Accurate and Efficient IDS,” Proc. 19th Ann. Computer Security Applications Conf. (ACSAC ’03), 2003, pp. 234-244.
[13] Y. Gu, A. McCallum, and D. Towsley, “Detecting Anomalies in Network Traffic Using Maximum Entropy Estimation,” Proc. Internet Measurement Conf. (IMC ’05), 2005, pp. 345-350, USENIX Assoc.
[14] S Franklin, A Graser, Is it an agent or just a program? in ECAI `96 Proceedingsof the Workshop on Intelligent Agents III, Agent Theories, Architectures, andLanguages (Springer, London, 1996)
[15] N Jaisankar, SGP Yogesh, A Kannan, K Anand, Intelligent Agent BasedIntrusion Detection System Using Fuzzy Rough Set Based Outlier Detection, SoftComputing Techniques in Vision Sci., SCI 395 (Springer, 2012), pp. 147–153.
[16] T Magedanz, K Rothermel, S Krause, Intelligent agents: an emergingtechnology for next generation telecommunications? In INFOCOM`96Proceedings of the Fifteenth Annual Joint Conference of the IEEE Computerand Communications Societies, San Francisco, 1996 Mar 24–28.
[17] W Zhang, S Teng, H Zhu, H Du, X Li, Fuzzy Multi-Class Support VectorMachines for Cooperative Network Intrusion detection. Proc. 9th IEEE Int.Conference on Cognitive Informatics (ICCI’10) (IEEE, Piscataway, 2010), pp. 811–818.
[18] L Zadeh, Role of soft computing and fuzzy logic in the conception, design and development of information/intelligent systems, in Computational Intelligence: Soft Computing and Fuzzy-neuro Integration with Applications, O Kaynak, L Zadeh, B Turksen, I Rudas. Proceedings of the NATO Advanced Study Institute on Soft Computing and its Applications held at Manavgat, Antalya, Turkey, 21–31 August 1996, volume 162 of NATO ASISeries (Springer, Berlin, 1998), pp. 1–9.
[19] M Moradi, M Zulkernine, A neural network based system for intrusiondetection and classification of attacks, in Proceedings of IEEE InternationalConference on Advances in Intelligent Systems – Theory and Applications,Luxembourg, vol. 148 (IEEE, Amsterdam, 2004), pp. 1–6
[20] S Sarasamma, Q Zhu, J Huff, Hierarchical Kohonen net for anomalydetection in network security. IEEE Transactions on System, Man,Cybernetics, Part B, Cybernetics 35(2), 302–312 (2005).
[21] O Linda, T Vollmer, M Manic, Neural network based intrusion detectionsystem for critical infrastructures, in Proceedings of IEEE International JointConference on Neural Networks, Georgia (IEEE, Amsterdam, 2009), pp. 102–109.
[22] C Cortes, V Vapnik, Support vector networks. Mach. Learn. 20, 1–25 (1995).
[23] S.-W. Lin, K.-C. Ying, C.-Y. Lee and Z.-J. Lee, "An intelligent algorithm with feature selection and decision rules applied to anomaly intrusion detection", Applied Soft Computing, vol. 12, (2012), pp. 3285-3290.
[24] VahidGolmah, An Efficient Hybrid Intrusion Detection System based on C5.0 and SVM, International Journal of Database Theory and Application Vol.7, No.2 (2014), pp.59-70.
[25] Zhai Jinbiao, Research on Intrusion Detection System Based on Clustering Fuzzy Support Vector Machine, International Journal of Security and Its Applications Vol.8, No.3 (2014), pp. 249-260.
[26] Md. Al Mehedi Hasan, Mohammed Nasser, Biprodip Pal, Shamim Ahmad, Support Vector Machine and Random Forest Modeling for Intrusion Detection System (IDS), Journal of Intelligent Learning Systems and Applications, 2014, 6, 45-52.
[27] Jashan Koshal, Monark Bag, Cascading of C4.5 Decision Tree and Support Vector Machine for Rule Based Intrusion Detection System, I. J. Computer Network and Information Security, 2012, 8, 8-20.
[28] B. Sujitha, V. Kavitha, “Layered Approach For Intrusion Detection Using Multiobjective Particle Swarm Optimization”, International Journal of Applied Engineering Research, vol.10, no.12, pp. 31999 – 32014, 2015.
[29] G. Creech, J. Hu, “A Semantic Approach to Host-Based Intrusion Detection Systems Using Contiguous and Discontiguous System Call Patterns”, IEEE Transactions on Computers, vol. 63, no. 4, pp. 807 – 819, 2014.

IELM, KDD cup’99, IDS, DoS.