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Implementation of Echo State Neural Network and Radial Basis Function Network for Intrusion Detection

Y. Kalpana, S. Purushothaman, R. Rajeswari

Abstract


Intrusion detection is the art of detecting computer abuse and any attempt to break into networks. As a field of research, it must continuously change and evolve to keep up with new types of attacks or adversaries and the ever-changing environment of the Internet. To make networks more secure, intrusion-detection systems (IDS) aims to recognize attacks. Artificial neural networks (ANN) based IDS were implemented and tested. The goal for using ANNs for intrusion detection is to generalize from incomplete data and able to classify data as being normal or intrusive. An ANN consists of a collection of processing elements that are highly interconnected. Given a set of inputs and a set of desired outputs, the transformation from input to output is determined by the weights associated with the inter-connections among processing elements. By modifying these interconnections, the network adapts to desired outputs. The ability of high tolerance for learning-by-example makes neural networks flexible and powerful in IDS. This paper has implemented Echo state neural network and Radial basis function applied to intrusion detection. The scope of the work includes using the available KDD database.

Keywords


Radial Basis Function (RBF) Networks Echo State Neural Networks (ESNN), KDD Features, Intrusion Detection.

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References


Beghdad R, 2007, Training all the KDD dataset to classify and detect attacks in International Journal on Neural and Mass parallel computing and Information Systems, Vol.17.

Botha M., Solms R., Utilizing Neural Networks For Effective Intrusion Detection, ISSA, 2004.

Chan, A., Ng W., Yeung D.S., and Tsang E., 2005, Multiple classifier system with feature grouping for intrusion detection: Mutual information approach, Lecture Notes in Artificial Intelligence, 3683, pp.141-148.

Chobrolu S., 2005A. Abraham, P. Johnson, feature deduction and ensemble design of intrusion detection systems, Elsevier computers and security, Vol.24, pp.195-307.

Cunningham R., and Lippmann R., 2000b, Improving Intrusion Detection performance using Keyword selection and Neural Networks, Computer Networks, Vol.34, No.4, pp.597-603.

Deepa V. Guleria, Chavan M.K., 2013, Intrusion Detection System Based On Conditional Random Fields, International Journal of Engineering Research and Technology, Vol.2, Issue 5, pp.653-660.

Dongli W., Yan Z, and Xiaoyang H., 2007, RBF neural network based model predictive control for freeway traffic systems, International Journal of Intelligent Systems Technologies and Applications, Vol.2, No.4, pp.370-388.

Gavrilis D., and Dermatas E., 2005, Real-time detection of distributed denial-of-service attacks using RBF networks and statistical features. Computer Networks and ISDN Systems, Vol.48, pp.235-245.

Gelenbe E., 1993, Learning in the recurrent random neural network, Neural Computation, Vol.5, pp.154-164.

Hoai-Vu Nguyen and Yongsun Choi, 2010, Proactive detection of DDoS attacks utilizing k-NN classifier in an anti-DDoS framework, International Journal of Electrical and Electronics Engineering, Vol.4, Issue 4, pp.247.

Jaeger H., The echo state approach to analyzing and training recurrent neural networks, German National Research Center for Information Technology, Tech. Rep.148, 2001.

Kayacik H., Zincir-Haywood A., and. Haywood M., 2005, Selecting features for intrusion detection: a feature relevance analysis on KDD99 intrusion detection datasets, Dalhousie University.

KDD Cup 1999 Intrusion Detection Data, http://kdd.ics.uci.edu/databases/kddcup99/kddcup99.html, 2010.

Kejie Lu, Dapeng Wu, Jieyan Fan , Sinisa Todorovic, 2007, Antonio Nucci, Robust and efficient detection of DDoS attacks for large scale internet, Science Direct, Computer Networks, pp.5036-5056.

Kumar S., 2010, Denial of Service Due to Direct and Indirect ARP Storm Attacks in LAN Environment. Journal of Information Security 01, Vol.2, pp.88–80.

Lappas T., 2007, Data Mining Techniques for (Network) Intrusion Detection System.

Lukosevicius M., and Jaeger H., Reservoir computing approaches to recurrent neural network training, Computer Science Review, pp.127–149. 2009.

Mum G., and Kim Y., 2006, network intrusion detection using statistical probability distribution, information systems and information technology, Vol.3984, pp.340-348.

Power R., 2002, CSI/FBI computer crime and security survey, Computer Security Journal, Vol.XVIII, No.2, pp.7-30.

Ranjan S., Swaminathan R., Uysal M., Nucci A., and Knightly E., 2009, DDoS-shield: DDoS resilient scheduling to counter application layer attacks, IEEE/ACM Transactions on Networking, Vol.17, pp.26-39.

Samaneh Rastegari, Iqbal Saripan M., and Mohd Fadlee A. Rasid, 2009, Detection of Denial of Service attacks against Domain Name System Using Neural Networks, International Journal of Computer Science Issues, Vol.6, No.1, pp.444-447.

Sapna S. Kaushik, Deshmukh P.R., 2011, Detection of Attacks in an Intrusion Detection System, International Journal of Computer Science and Information Technologies, Vol.2, No.3, pp.982-986.

Shevtekar A., Anantharam K., and Ansari N., 2005, Low-rate TCP denial-of-service attack detection at edge routers, IEEE Communications Letters, Vol.9, pp.363-365.

Sung A., and Mukkamala S., 2004, The feature selection and intrusion detection problems, Lecture Notes in Computer Science, 3321, pp.468-482.

Tao peng, Christopher leckie, and Kotagiri ramamohanarao, Survey of Network-Based Defense Mechanisms Countering the DoS and DDoS Problems, ACM Computing Surveys, Vol.39, No.1, Article 3, 2007.

Theuns Verwoerd, Ray Hunt, 2002, Intrusion detection techniques and approaches, Computer Communications, Vol.25, pp.1356-1365.

Verwoed T., and Hunt R., 2002, Intrusion detection techniques and approaches, Elsevier: computer communications, Vol.25, No.10, pp.1356-1365.

Zenghui L., Yingxu L., 2009, A Data Mining Framework for Building Intrusion Detection Models Based on IPv6, Proceedings of the 3rd International Conference and Workshops on Advances in Information Security and Assurance. Seoul, Korea, Springer- Verlag.


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