MULTIPLE NASH REPUTATION CROSS LAYER CLASSIFICATION FRAMEWORK FOR COGNITIVE NETWORKS

Authors:

Ganesh Davanam,T. Pavan Kumar,M. Sunil Kumar,

DOI NO:

https://doi.org/10.26782/jmcms.2020.08.00033

Keywords:

Trust,Reputation,Cross Layer attack,Cognitive Radio Networks,Multiple Nash Equilibrium,

Abstract

Cognitive Radio Networks (CRNs) are new type of communication networks which solves the problems of spectrum utilization and channel assignments in an important manner. Cognitive users are two types i.e Primary and Secondary users. Secondary users use the unused spectrum which is not used by the primary user i.e unlicensed users uses the licensed bandwidth with their permission. Hence, Trust and Reputation management of secondary users has gained more attention. Mainly Reputation management models are required for CRNs to clearly identify whether the Secondary user is Malicious or trusted. If the secondary user is malicious he will attack the network at different layers and degrades the performance. In this paper, a method called Multiple Nash Reputation (MNR) method is proposed to secure the CRN at two different layers namely physical and network. First, trust is separately calculated for each CR user at two different layers, physical layer and network layer using trust parameters. After that the classification of malicious and normal user is made by applying the Multiple Nash Game Theory model. The performance of MNR method is evaluated based on Energy consumption and detection accuracy.

Refference:

I. Brochure, “Coexistence of wireless systems in automation technology,” in Proc. ZVEI – Central Association for Electrical and Electronic Industry, Germany, April 2018.(1)

II. Deanna Hlavacek, J. Morris Chang, “A layered approach to cognitive radio network security: A survey”, Computer Networks, Elsevier, Oct 2014

III. Ernesto Cadena Muñoz, Enrique Rodriguez-Colina, Luis Fernando Pedraza, Ingrid Patricia Paez, “Detection of dynamic location primary user emulation on mobile cognitive radio networks using USRP”, EURASIP Journal on Wireless Communications and Networking, Springer Open, Feb 2020

IV. GaneshDavanam,T.Pavan Kumar,M.Sunil Kumar,”Mean Bid Trust Cross Layer Trust Evaluation Model for Cognitive Radio Networks”,International Journal of Advanced Science and Technology, Vol. 29, No. 5, (2020), pp. 11450-11461

V. G. Staple and K. Werbach, “The end of spectrum scarcity [spectrum allocation and utilization],” IEEE Spectrum, vol. 41, no. 3, March 2010.(4)

VI. Jaydip Sen, “A Survey on Security and Privacy Protocols for Cognitive Wireless Sensor Networks”, Journal of Network and Information Security, Jun 2013

VII. Jihen Bennaceur Hanen Idoudi Leila Azouz Saidane, “Trust management in cognitive radio networks: A survey”, International Journal of Network Management, Wiley, Aug 2017(10)

VIII. Jithin Jagannath, Sean Furman, Tommaso Melodia, Andrew Drozd, “Design and Experimental Evaluation of a Cross-Layer Deadline-Based Joint Routing and Spectrum Allocation Algorithm”, IEEE Transactions on Mobile Computing, Vol. 18, No. 8, Aug 2019 (2)

IX. Linyuan Zhang, Guoru Ding, Qihui Wu, Yulong Zou, Zhu Han, Jinlong Wang, “Byzantine Attack and Defense in Cognitive Radio Networks: A Survey”, Elsevier, Jun 2015

X. Mee Hong Ling, Kok-Lim Alvin Yau, Geong Sen Poh, “Trust and reputation management in cognitive radio networks: a survey”, Security and Communication Networks, Wiley, Nov 2013

XI. Mitola, “Cognitive Radio Architecture Evolution,” in Proc. of the IEEE, vol. 97, no. 4, April 2009.(5)

XII. MouniaBouabdellah, Naima Kaabouch, Faissal El Bouanani, Hussain Ben-Azza, “Network layer attacks and countermeasures in cognitive radio networks: A survey”, Journal of Information Security and Applications, Elsevier, Jul 2018

XIII. Nadine Abbas, Youssef Nasser, Karim El Ahmad, “Recent advances on artificial intelligence and learning techniques in cognitive radio networks”, EURASIP Journal onWireless Communications and Networking, Springer, Jul 2015

XIV. Quanyan Zhu, Stefan Rass, “Game Theory Meets Network Security”, ACM, Oct 2019

XV. Saim Bin Abdul Khaliq, Muhammad Faisal Amjad, Haider Abbas, Narmeen Shafqat, Hammad Afzal, “Defence against PUE attacks in ad hoc cognitive radio networks: a mean field game approach”, Telecommunication Systems, Springer, May 2018 (3)

XVI. Wang Zhendong, Wang Huiqiang, Zhu Qiang, “A Trust Game Model and Algorithm for Cooperative Spectrum Sensing in Cognitive Radio Networks”, International Journal of Future Generation Communication and Networking Vol. 8, No. 3 (2015).(7)

XVII. W.Saad, et al., “Coalitional game theory for communication networks,” in IEEE Signal Processing Magazine, vol. 26, no. 5, Sept 2017.(8)

XVIII. Y. Zhao, S. Mao, J. O. Neel, and J. H. Reed, “Performance evaluation of cognitive radios: metrics, utility functions, and methodology,” in Proc. Of the IEEE, vol. 97, no. 4, April 2009.(6)

XIX. Z. Han et al., Game Theory in Wireless and Communication Networks: Theory, Models, and Applications, Cambridge Press, Cambridge, 2012.(9)

XX. Z. Jin, S. Anand, K. P. Subbalakshmi, “Impact of Primary User Emulation Attacks on Dynamic Spectrum Access Networks”, IEEE Transactions on Communication, Vol. 60, Issue 9, Sep 2012

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