Improved Ensemble Technique based on Support Vector Machine and Neural Network for Intrusion Detection System

Authors

  • Aafreen K. Siddiqui M.Tech Scholar, Department of CSE ASCT, BHOPAL
  • Tanveer Farooqui Assistant Professor Department of CSE , ASCT, BHOPAL

DOI:

https://doi.org/10.24113/ijoscience.v3i12.9

Keywords:

Cyber-attack Classification, Ensemble Technique, SVM, NN, KDDCUP99, K-Nearest Neighbour.

Abstract

Intrusion Detection System (IDS) is a tool for anomaly detection in network that can help to protect network security. At present, intrusion detection systems have been developed to prevent attacks with accuracy Intrusion detection is a process for Cyber-attack classification and detection process is based on the fact that intrusive activities are different from normal system activities. Its detection is a very complex process in network security. In current network security scenario various types of cyber-attack family exist, some are known family and some are unknown one. The detection of known attack is not very difficult it generally uses either signature base approach or rule based approach, but to find out the unknown one is a challenging task. One of the major developments in machine learning in the past decade is the ensemble method, which finds highly accurate classifier by combining many moderately accurate component classifiers. This paper addresses using of an ensemble classification methods for intrusion detection. The paper proposes a cascaded support vector machine classifier or an improved ensemble classifier using multiple kernel function. The multiple kernel is Gaussian in nature. The graph based neural network technique used for feature collection of different types of cyber-attack data. The proposed algorithm is very efficient in comparison of pervious method.

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References

Shailendra Singh, Sanjay Silakari “An Ensemble Approach for Cyber Attack Detection System: A Generic Framework” 14th ACIS, IEEE 2013. Pp 79-85.[2] X. Li et al., “Smart Community: An Internet of Things Application,” IEEE Commun. Mag., Vol. 49, no. 11, 2011, pp. 68–75.

V. Bapuji, R. Naveen Kumar2,Dr. A. Govardhan, S.S.V.N. Sarma “Soft Computing and Artificial Intelligence Techniques for Intrusion Detection System” Vol 2, No.4, 2012, pp 24-33.

Hoa Dinh Nguyen, Qi Cheng “An Efficient Feature Selection Method For Distributed Cyber Attack Detection and Classification” IEEE 2013. pp 1-6.

Bimal Kumar Mishra,Hemraj Saini “Cyber Attack Classification using Game Theoretic Weighted Metrics Approach” World Applied Sciences Journal 7, 2009. Pp 206-215.

Xu Li, Inria Lille, Xiaohui Liang, Xiaodong Lin, Haojin Zhu “Securing Smart Grid: Cyber Attacks,Countermeasures, and Challenges” IEEE Communications Magazine IEEE 2012. Pp 38-46.

Haitao Du, Christopher Murphy, Jordan Bean, Shanchieh Jay Yang “Toward Unsupervised Classification of Non-uniform Cyber Attack Tracks” International Conference on Information Fusion 2009. Pp 1919-1925.

Abhishek Jain And Ashwani Kumar Singh “Distributed Denial Of Service (Ddos) Attacks - Classification And Implications”journal of Information and Operations Management Vol-3 2012. Pp 136– 140.

Dewan Md. Farid, Nouria Harbi, Emna Bahri, Mohammad Zahidur Rahman, Chowdhury Mofizur Rahman “Attacks Classification in Adaptive Intrusion Detection using Decision Tree” World Academy of Science, Engineering and Technology, 2009. Pp 86-91.

Chee-Wooi Ten, Govindarasu Manimaran “Cybersecurity for Critical Infrastructures:Attack and Defense Modeling “IEEE Transactions on Systems, Vol-40 IEEE 2010. Pp 853-865.

Mohammad A. Faysel , and Syed S. Haque “Towards Cyber Defense: Research in Intrusion Detection and Intrusion Prevention Systems” IJCSNS, Vol-7 2010. Pp 316-325.

Shailendra Singh, Sanjay Agrawal, Murtaza,A. Rizvi and Ramjeevan Singh Thakur “ Improved Support Vector Machine for Cyber Attack Detection” WCECS IEEE, 2011. Pp 1-6.

Real-time Misuse Detection Systems, Proceedings of the IEEE on Information, 2004.

Vineet Richhariya , Dr. J.L.Rana ,Dr. R.C.Jain ,Dr. R.K.Pandey” Design of Trust Model For Efficient Cyber Attack Detection on Fuzzified Large Data using Data International Journal of Computer Applications (0975 – 8887) Volume 103 – No.11, October 2014Mining techniques” IJRCCT Vol 2, Issue 3, 2013. Pp 126-132.

Deepak Rathore and Anurag Jain “Design Hybrid method for intrusion detection using Ensemble cluster classification and SOM network” in International Journal of Advanced Computer Research Volume-2 Number-3 Issue-5 September-2012.

M Govindarajan and RM.Chandrasekaran ”Cyber-Attack Classification Using Improved Ensemble Technique Based On Support Vector Machine and Neural Network”, Proceding of the World Congress on Engineering and Computer Science 2012 Vol IWCECS 2012, October 24-26, 2012, San Francisco, USA.

Freund, Y. and Schapire, R. (1995) A decision-theoretic generalization of on-line learning and an application to boosting. In proceedings of the Second European Conference on Computational Learning Theory, pp 23-37.

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Published

12/29/2017

How to Cite

Siddiqui, A. K., & Farooqui, T. (2017). Improved Ensemble Technique based on Support Vector Machine and Neural Network for Intrusion Detection System. SMART MOVES JOURNAL IJOSCIENCE, 3(12), 1–7. https://doi.org/10.24113/ijoscience.v3i12.9