Enhanced Hybrid Model of Support Vector-Grey Wolf Optimizer Technique to Improve the Classifier’s Detection Accuracy in Designing the Efficient Intrusion Detection Model

Authors

  • Vidhya Sathish research scholar department of computer applications b.s.abdur rahman university vandalur, chennai-48
  • P. Sheik Abdul Khader

Keywords:

Grey Wolf Optimizer, Support Vector Machine, Enhanced hybrid approach, Detection accuracy.

Abstract

Research over designing the intrusion detection systems happening during a decade in tremendous way. The specified reason is to study the intrusion presence over network traffic. Based on this, analysis and classification of traffic pattern refined, to projects the importance of enhanced detection approach. Herewith, the methodologies deployed to better understand of ‘abnormal’ traces and paved the way for descriptive analysis. From the literature review of study, clarifies that hybrid computation approach entitled as fine-tuned work in analyzing intrusion trace illustrative when compared to single approach. The hazard of hybrid methodology is to be high resource computation which leads to step down in its absolute approach. The proposed research has framed to improve the support vector machine’s classifier approach in designing the efficient detection model. This happens by additive support of grey wolf optimizer algorithm with the aim to improve the classifier’s detection accuracy in exact classification of ‘normal’ and ‘abnormal’ instance traces from the modified KDDCUP99 intrusion dataset in minimal learning time. Experimentation of work outperform using WEKA simulator tool in WINDOWS operating system background.

References

VidhyaSathish and P.Sheik Abdul Khader, “Deployment of Proposed Botnet Monitoring Platform using Online Malware Analysis for Distributed Environmentâ€, Indian Journal of Science and Technology, vol. 7, no. 8, pp.1087-1093, 2014.

Sundusjuma, Zaitonmuda, M.A. Mohamed, and Warusia Yassin, “Machine Learning Techniques For Intrusion Detection System: A Reviewâ€, Journal of Theoretical and Applied Information Technology, vol. 72, no. 3, pp. 422-429, 2015.

VidhyaSathish, and P.Sheik Abdul Khader, “ An Investigational Study on Intrusions based Traffic Identification using WEKA tool†, International Journal of Applied Engineering Research , vol. 10, no. 15, pp.35160-35166, 2015.

J.Singh and M.J.Nene, “A Survey on Machine Learning Techniques for Intrusion Detection Systemsâ€, International Journal of Advanced Research in Computer and Communication Engineering, vol. 2, no. 11, pp.4349-4355, 2013.

S.Mirjalili, S.M.Mirjalili and A.Lewis, “Grey Wolf Optimizerâ€, Advances in Engineering Software, Elsevier, vol. 69, pp.46-61, Mar 2014.

P. Sapate, and S.A.Raut, “Survey on Classification techniques for Intrusion Detectionâ€, Proceedings of the Fourth International Conference on Advances in Computing & Information Technology (ACITY 2014), May 24-25, pp. 223-231, 2014.

Harsimran Kaur, “Algorithm used in Intrusion Detection Systems: A Reviewâ€, International Journal of Innovative Research in Computer and Communication Engineering., vol. 2, no. 5, pp.4438-4446, 2014.

N.K.Sinha, G.Kumar, and K.Kumar, “A Review on Performance Comparison of Artificial Intelligence Techniques used for Intrusion Detectionâ€, International Conference on Communication, Computing & Systems (ICCCS-2014)., pp.209-214, 2014.

S.K.Jonnalagadda, and I.R.P. Reddy, “A Literature Survey and Comprehensive study of Intrusion Detectionâ€, International Journal of Computer Applications, vol. 81, no. 16, pp.40-47, 2013.

H.Liao, C.R.Lin, Y. Lin, and K.Tung, “Intrusion Detection System: A Comprehensive reviewâ€, Journal of Network and Computer Applications, vol. 36, no. 1, pp.16-24, 2013.

Prof. N.S. Chandolikar, and Prof. V.D. Nandavadekar, “Selection of Relevant Feature for Intrusion attack classification by analyzing KDDCUP99â€, MIT International Journal of Computer Science and Information Technology. Vol. 2, no. 2, pp. 85-90, 2012.

C.Kolias, G.Kambourakis, and M.Maragoudakis, “Swarm Intelligence in Intrusion Detection: A Surveyâ€, Computers & Security, vol . 30 no. 8, pp.625-642, 2011.

M.Bahrololum, E. Salahi, and M. Khaleghi, “An Improved Intrusion Detection Technique based on two strategies using Decision Tree and Neural Networkâ€, Journal of Convergence Information Technology, vol.4, no. 4, pp.96-101, 2009.

D.Farid, J.Darmont, N.Harbi, N.H.Hoa, and M.Z.Rahman, “Adaptive Network Intrusion Detection Learning: Attribute Selection and Classificationâ€, Proceedings of the International Conference on Computer Systems Engineering (ICCSE 09), vol. 60, pp.154-158, 2009.

N.Ye, and X.Li, “A Scalable Clustering Technique for Intrusion Signature Recognitionâ€, Proceedings of the 2001 IEEE Workshop on Information Assurance and Security, pp.1-4, 2001.

J. Zhang, and M. Zulkernine, “Anomaly based Network Intrusion Detection with Unsupervised Outlier Detectionâ€, IEEE International Conference on Communications, pp.2388-2393, 2006.

G.Stein, B.Chen, A.S.Wu, and K.A.Hua, “Decision tree classifier for Network Intrusion Detection with Genetic Algorithm based feature selectionâ€, In: Proceedings of the 43rd annual south east regional conference ACM. 2, pp.136-141, 2005.

W. Tian, and J. Liu, “Network Intrusion analysis with Neural Network and Particle Swarm Optimization algorithmâ€, In: 2010 Chinese IEEE Control and Decision Conference (CCDC), pp.1749-1752, 2010.

G.Wang, J.Hao, J. Ma, and LHuang, “A new approach to intrusion detection using Artificial Neural Networks and fuzzy clusteringâ€, Expert Systems with Applications. vol.37, no.9, pp. 6225-6232, 2010.

M.Panda, and M.RPatra, “A Comparative Study of datamining algorithms for network intrusion detectionâ€, First International Conference on Emerging Trends in Engineering and Technology, pp.504-507, 2008.

M.Abadi, and S. Jalali, “An ant colony optimization algorithm for network vulnerability analysisâ€, Iranian Journal for Electrical and Electronic Engineering, vol.2, no.3, pp.106-120, 2006.

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Published

2016-03-05

How to Cite

Sathish, V., & Khader, P. S. A. (2016). Enhanced Hybrid Model of Support Vector-Grey Wolf Optimizer Technique to Improve the Classifier’s Detection Accuracy in Designing the Efficient Intrusion Detection Model. Asian Journal of Applied Sciences, 4(1). Retrieved from https://ajouronline.com/index.php/AJAS/article/view/3588