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Anomaly Detection Using Feed Forward and Radial Basis Function Neural Network

D. Amutha Guka

Abstract


This paper presents the design and development of intelligent system for anomaly detection using neural networks. This paper presents the application of Multi Layer Feed Forward (MLFF) neural network approach and Radial Basis Function (RBF) neural network for anomaly detection. The KDD Cup 1999 dataset is considered as a benchmark dataset for evaluating this algorithm. The network is developed to detect a total of twenty three, in which twenty two intruders and a normal. The training and testing data required to develop the model were randomly selected subset that contain both attacks and normal records from KDD Cup 1999 dataset. Principal Component Analysis based feature extraction method is used for dimensionality reduction. An integration platform used for developing this intelligent system is Visual Basic and Mat lab. The performance of the anomaly detection system using RBF is found to be best when compared to the MLFF results. The comparison of results and consistency are presented in this paper.

Keywords


Anomaly Detection, Multilayer Feed Forward Neural Networks, Radial Basis Function Neural Network

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References


Franciszek Seredynski and Pascal Bouvry “Anomaly detection in TCP/IP networks using immune systems paradigm”, Computer Communications, No. 30, pp. 740-749, 2007.

D. Huang and Tommy.W.S.Chow “Searching optimal feature subset using mutual information”, ESANN’2003 Proceedings-European symposium on Artificial Neural Networks, Brugs, pp. 161-166, 2003.

Mansour Sheikhan and Amir Ali Sha‟bani “Fast Neural Intrusion Detection System Based on Hidden Weight Optimization Algorithm and Feature Selection”, World Applied Sciences Journal, Special issue of Computers & IT, No. 7, pp. 45-53, 2009.

M. Saniee Abaesh, J. Habibi. and C. Lucas. “Intrusion detection using a fuzzy genetics-based learing algorithm”, Journal of Network and Computer Applications, No. 178, pp.3024-3042, 2007.

T. Simen, Powers and Jun He “A hybrid artificial immune system and Self Organising Map for network intrusion detection”, Information Sciences, No. 178, pp. 3024-3042, 2008.

SuseelaT.sarasamma, Qiuming A.Zhu and Julie Huff “Hierarchical Kohonenen Net for anomaly detection in Network Security”, IEEE Transactions on systems, man and cybernetics , vol.35. No. 2, pp. 302-312, 2005.

Roberto Battiti “Using Mutual Information for selecting feature in supervised neural net learning”, IEEE Transaction on Neural networks, Vol.5, No.4, pp. 537-550, 1994.

Guisong Liu, Zhang yi, Shangming Yang “A hierarchical intrusion detection model based on the PCA neural networks”, Neurocomputing, No.70, pp. 1561-1568, 2007.

Khalil EI-Khatip “Impact of Feature Reduction on the Efficiency of Wireless Intrusion Detection Systems”, IEEE Transactions on Parallel and Distributed Systems, Vol. 21, No. 8, pp.1143-1149, 2010.

Jaeik Cho, Changhoon Lee, Sanghyun Cho, Jung Hwan Song, Jongin Lim and Jongsub Moon “A statistical model for network data analysis: KDD CUP 99‟ data evaluation and its comparing with MIT Lincoln Laboratory network data”, Simulation Modelling Practice and Theory, No. 178, pp. 3024-304, 22010.

V. Bolon-Canedo, N. Sanchez-Marono, and Alonso-Betanzos “Feature selection and classification in multiple class datasets: An application to KDD Cup 99 dataset”, Expert Systems with Applications, No. 38, pp. 5947-5957, 2011.

The UCI KDD Archive, KDD Cup 99 data, [Online] available at: http://kdd.ics.uci.edu//databases/kddcup99.html.

B.Yegnanarayana, Artificial neural networks, Prentice-Hall of India Pvt. Ltd., New Delhi, India, 1999.

S.N.Sivanandam, S.Sumathi, and S.N.Deepa Introduction to Neural Networks using Matlab 6.0, Tata McGraw-Hill Publishing Company limited, New Delhi, 2006.

S.Rajakarunakarn, D.Devaraj and K.Suryaprakasa Rao “Fault detection in centrifugal pumping systems using neural networks”, International Journal of Modelling, Identification and control, Vol. 3, No. 2, pp. 131-138, 2008.

D. Devaraj, B.Yegnanarayana, K.Ramar “Radial networks for fast contingency ranking,” Electric Power and Energy Systems Journal, Vol. 24, pp. 387-395, 2002.

Guisong Liu, Zhang Hi and Shangming Yang “ A hierarchical intrusion detection model based on the PCA neural networks,” Neuro Computing, 70, pp. 1561-1568, 2007.

C. Xiang, S.M. Lim, Design of multiple-level hybrid classifier for intrusion detection system, in: Proceedings of the IEEE Workshop Machine Learning for Signal Processing, pp. 117–122, September 2005.

Z. Chunlin, J. Ju, K. Mohamed, Intrusion detection using hierarchical neural networks, Pattern Recognition Lett. 26 (6), pp. 779–7912005.

G. Liu, Z. Yi, Intrusion Detection Using PCASOM Neural Networks, in: Proceedings of ISNN2006, Lecture Notes in Computer Science, vol. 3973, Springer, Berlin, Heidelberg, pp. 240–245, 2006.

C. Wun-Hua, H. Sheng-Hsun, S. Hwang-Pin, Application of SVM and ANN for intrusion detection, Comput. Oper. Res. 32 (10), pp. 2617–2634, 2005.

L. Pavel, D. Patrick, S. Christin, K. Rieck, in: Learning Intrusion Detection: Supervised or Unsupervised, Lecture Notes in Computer Science, vol. 3617, Springer, Berlin, Heidelberg, pp. 50–57, 2005.

M. Shyu, S. Chen, K. Sarinnapakorn, L. Chang, A Novel Anomaly Detection Scheme Based on Principal Component Classifier, in: Proceedings of ICDM‟03, pp. 172–179. 2003.


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