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Reseach Article
A New Fuzzy based Approach for Destabilization of Terrorist Network
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 129 - Number 13 |
| Year of Publication: 2015 |
| Authors: Suraksha Tiwari, Shilky Shrivastava, Manish Gupta |
10.5120/ijca2015907068
|
Suraksha Tiwari, Shilky Shrivastava, Manish Gupta . A New Fuzzy based Approach for Destabilization of Terrorist Network. International Journal of Computer Applications. 129, 13 ( November 2015), 21-27. DOI=10.5120/ijca2015907068
Abstract
The wide scope of Social network analysis has led the law require agencies to study the performance of social network. The main objective of this study is to identify the key nodes (suspicious nodes) present in the network. Recently, Terrorist Network Mining (special branch of Social Network Analysis) has gained considerable prominence in the data mining community because of its relevance to the real scenario situation and need. This paper proposes a new Fuzzy based optimization mechanism. The proposed optimization mechanism is appropriate for operative optimization of big social network holding non terrorist and terrorist nodes. At the time of the optimization procedure elimination of non-terrorist nodes from the network has been performed and the optimization resultant represents only the reduced / optimized graph holding only the collection of potential nodes. Fuzzy Rule based system for the security of the cyber is a system that contains a rule depository and a mechanism for the accessing and also running the rules. The depository is generally created with a group of the related rule sets. The goal of this study is to progress of a fuzzy rule based practical indicator for the security of cyber with the use of an skillful method which is named System of the Fuzzy Rule Based Cyber Expert. Rule based systems employ fuzzy rule to the automate complex procedures. A common cyber threats expected for the cyber specialists are used as linguistic variables in this paper. However, these algorithm results in fruitful outcomes, there is a need to propose a typical algorithm for destabilization. In consideration to this, the present paper proposes a novel algorithm for destabilization of terrorist network revealing the hidden hierarchy followed by the network.
References
- Arun K. Pujari (2001), “Data Mining Techniques”, Universities Press.
- Jiawei Han & Micheline Kamber (2006) Data Mining;Concepts and Techniques, Second Edition, MorFAn Kaufmann Publishers.
- Muhammad Akram Shaikh, Wang Jiaxin, (2006) “InvestiFAtive Data Mining: Identifying Key Nodesin Terrorist Networks”.
- M. A. Shaikh, and W. Jiaxin, (2006), “InvestiFAtive Data Mining: Identifying Key Nodes in Terrorist Networks”, Multitopic Conference, INMIC '06, pp. 201-207 IEEE 2006.
- U. K. Wiil, N. Memony, and P. Karampelas, (2010), “Detecting New Trends in Terrorist Networks”, In Proceedings of International Conference on Advances in Social Networks Analysis and Mining, 2010.
- K. M. Carley, J. ReminFA, and N. Kamneva, (2003), “Destabilizing Terrorist Networks”, In Proceedings of the 8th International Command and Control Research and Technology Symposium, 2003.
- P. V. Fellman and R. Wright: Modeling, (2003), “Terrorist Networks - Complex Systems at the Mid-Range”, In: Proceedings of Complexity, Ethics and Creativity Conference, LSE (2003).
- Y. Elovici, A. Kandel, M. Last, B. Shapira and O. Zaafrany, (2004), “Using Data Mining Techniques for Detecting Terror-Related Activities on the Web”, In: Proceedings of Journal of Information Warfare (2004).
- N. Memon and H. L. Larsen, (2006), “Structural Analysis and Destabilizing Terrorist Networks”, In Proceedings of The First International Conference on Availability, Reliability and Security, 2006. ARES 2006, IEEE 2006.
- N. Memon, H. L. Larsen, D. L. Hicks, and N. Harkiolakis, (2008), “Detecting Hidden Hierarchy in Terrorist Networks: Some Case Studies”, In Proceedings of Springer-Verlag Berlin Heidelberg 2008, ISI 2008 Workshops, LNCS 5075, pp. 477–489, 2008.
- L.A. Zadeh, “Outline of a New Approach to the Analysis of Complex Systems and Dccision Processes,” IEEE Trans. Systems, Man and Cybmzetics, pp. 28-44.
- Zadeh, L. A. et al. 1996 Fuzzy Sets, Fuzzy Logic, Fuzzy Systems, World Scientific Press, ISBN 981-02-2421-4.
- L.A. Zadeh, “Fuzzy sets”, Information Control, vol.8, pp.338-353,1965.
- E.H. Mamdani, and S. Assilian, “An experiment in linguistic synthesis with a fuzzy logic controller”, Int. J. Man-Mach. Stud., vol.7, pp.1-13, 1975.
- P. Chapman, J. Clinton, T. Khabaza, T. Reinartz, and R. Wirth. The CRISP-DM Process Model, 1999 Available from http://www.ncr.dk/CRISP/.
- J.C. Bezdek, J.M. Keller, R. Krishnapuram, and N. Pal. Fuzzy Models and Algorithms for Pattern Recognition and Image Processing. The Handbooks on Fuzzy Sets. Kluwer, Dordrecht, Netherlands 1999
- F. H¨oppner, F. Klawonn, R. Kruse, and T. Runkler. Fuzzy Cluster Analysis. J. Wiley & Sons, Chichester, United Kingdom 1999.
- Maheshwari S., Tiwari A: “A Novel Genectic based Framework for the Detection and Destabilization of Influencing Nodes in Terrorist Network,” Computational Intelligence in Data Mining- Vol. 1, Smart Innovation, System and Technologies 31, DOI 10.1007/978-81-322-2205-7_53, pp 573-582(2015).
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