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A Co-operative Game Theoretic Approach to Improve the Intrusion Detection System in a Network using Ant Colony Clustering

International Journal of Computer Applications
© 2014 by IJCA Journal
Volume 87 - Number 14
Year of Publication: 2014
D. P. Jeyepalan
E. Kirubakaran

D P Jeyepalan and E Kirubakaran. Article: A Co-operative Game Theoretic Approach to Improve the Intrusion Detection System in a Network using Ant Colony Clustering. International Journal of Computer Applications 87(14):19-22, February 2014. Full text available. BibTeX

	author = {D. P. Jeyepalan and E. Kirubakaran},
	title = {Article: A Co-operative Game Theoretic Approach to Improve the Intrusion Detection System in a Network using Ant Colony Clustering},
	journal = {International Journal of Computer Applications},
	year = {2014},
	volume = {87},
	number = {14},
	pages = {19-22},
	month = {February},
	note = {Full text available}


Making a network foolproof is a very important task that every Intrusion Detection System should provide to the network. Areas of deployment of an IDS is also an important task that helps in efficient functioning of the system. Deploying the IDS in all the systems is a very inefficient strategy that reduces the performance of the entire network, while deploying the IDS in inappropriate or inefficient nodes leads to the system becoming vulnerable to attacks. The current proposal deals with improving the effectiveness of an Intrusion Detection System by selecting the appropriate candidates in the network. The candidacy selection is performed by initially clustering the nodes using Ant Colony Clustering algorithm and using Game Theoretical approaches for selection of heads and monitoring the environment for changes.


  • Lumer E. ,Faieta B. ,1994,"Diversity and adaptation in populations of clustering ants", in: J. -A. Meyer, S. W. Wilson(Eds. ), Proceedings of the Third International Conference on Simulation of Adaptive Behavior: From Animats,Vol. 3, MIT Press/Bradford Books, Cambridge, MA, pp. 501~508.
  • Handl, J. and Meyer, B. ,2002,"Improved ant-based clustering and sorting in a document retrieval interface",PPSN VII, LNCS 2439.
  • Maoguo Gong and Liefeng Bo,2007,"Density-Sensitive Evolutionary Clustering", The 11th Pacific- sia Conference on Knowledge Discovery and Data Mining, Springer-Verlag Berlin Heidelberg,pp. 507–514.
  • Ling Chen, Xiao-Hua Xu,Yi-Xin Chen ,26-29 August 2004,"An Adaptive Ant Colony Clustering Algorithm", Proceedings of the Third International Conference on Machine Learning and Cybernetics, Shanghai.
  • S. Nithya, R. Manavalan ,2012,"An Ant Colony Clustering Algorithm Using Fuzzy Logic", International Journal of Soft Computing And Software Engineering (JSCSE),2012, e-ISSN: 2251-7545, Vol. 2,No. 5.
  • Jamaludin Sallim , Rosni Abdullah, Ahamad Tajudin Khader," An Improved Ant Colony Optimization Algorithm for Clustering Proteins in Protein Interaction Network".
  • O. A. Mohamed Jafar and R. Sivakumar , October 2010,"Ant-based Clustering Algorithms: A Brief Survey", International Journal of Computer Theory and Engineering,Vol. 2, No. 5 , 1793-8201.
  • Hadi Otrok, Noman Mohammed, Lingyu Wang, Mourad Debbabi, Prabir Bhattacharya ,2008,"A game-theoretic intrusion detection model for mobile ad hoc networks",Computer Communications 31 (2008) 708–721.
  • Wenying Fenga,Qinglei Zhangc, Gongzhu Hud, Jimmy Xiangji Huange ,2014,"Mining network data for intrusion detection through combining SVMs with ant colony networks", Future Generation Computer Systems.
  • Chih-Fong Tsai,Yu-Feng Hsu , Chia-Ying Lin , Wei-Yang Lin,2009," Intrusion detection by machine learning: A review", Expert Systems with Applications 36 (2009) 11994–12000.
  • Shahaboddin Shamshirband , Nor Badrul Anuar , Miss Laiha Mat Kiah , Ahmed Patel ," An appraisal and design of a multi-agent system based cooperative wireless intrusion detection computational intelligence technique", Engineering Applications of Artificial Intelligence 26 (2013) 2105–2127.
  • D. P. Jeyepalan, E. Kirubakaran ,April 2013,"A Novel Graph Based Clustering Approach for Network Intrusion Detection", International Journal of Computational Intelligence and Information Security, Vol. 4 No. 4,ISSN: 1837-7823.
  • Qiang, W. , Vasileios, M,2005,"A Clustering Algorithm for Intrusion Detection",The SPIE Conference on Data Mining, Intrusion Detection, Information Assurance, and Data Networks Security, Florida, vol. 5812, pp. 31–38.
  • Joshua Oldmeadow, Siddarth Ravinutala, Christopher Leckie,2004,"Adaptive Clustering for Network Intrusion Detection", Springer-Verlag Berlin Heidelberg,PAKDD 2004, LNAI 3056, pp. 255–259.
  • XlONG Jiajun, LI Qinghua, TU Jing,2006,"A Heuristic Clustering Algorithm for Intrusion Detection Based on Information Entropy", Wuhan University Journal Of Natural Sciences, Vol. 11 No. 2 2006 355-359.
  • Maria C. V. Nascimento, Andre C. P. L. F. Carvalho, J,2011,"A Graph Clustering Algorithm Based On A Clustering Coefficient For Weighted Graphs", Brazil Computer Society,17:19–29DOI 10. 1007/s13173-010-0027.