Journal of Platform Technology

ICT Platform Society (ICT플랫폼학회)
권호 표지

Q Learning MDP Approach to Mitigate Jamming Attack Using Stochastic Game Theory Modelling With WQLA in Cognitive Radio Networks

  • Vimal, S. (Department of Computer Science and Engineering, Ramco Institute of Technology) ;
  • Robinson, Y. Harold (School of Information Technology and Engineering, Vellore Institute of Technology) ;
  • Kaliappan, M. (Department of Computer Science and Engineering, Ramco Institute of Technology) ;
  • Pasupathi, Subbulakshmi (School of Computing, Scope, VIT University, Chennai Campus) ;
  • Suresh, A. (Department of CSE, SRM University)
  • Received : 2021.02.10
  • Accepted : 2021.03.25
  • Published : 2021.03.30
Download PDF
⟨ Previous Next ⟩

Abstract

Cognitive Radio network (CR) is a promising paradigm that helps the unlicensed user (Secondary User) to analyse the spectrum and coordinate the spectrum access to support the creation of common control channel (CCC). The cooperation of secondary users and broadcasting between them is done through transmitting messages in CCC. In case, if the control channels may get jammed and it may directly degrade the network's performance and under such scenario jammers will devastate the control channels. Hopping sequences may be one of the predominant approaches and it may be used to fight against this problem to confront jammer. The jamming attack can be alleviated using one of the game modelling approach and in this proposed scheme stochastic games has been analysed with more single users to provide the flexible control channels against intrusive attacks by mentioning the states of each player, strategies ,actions and players reward. The proposed work uses a modern player action and better strategic view on game theoretic modelling is stochastic game theory has been taken in to consideration and applied to prevent the jamming attack in CR network. The selection of decision is based on Q learning approach to mitigate the jamming nodes using the optimal MDP decision process

Keywords

References

  1. SiamakDastangoo, Carl E. Fossa, Youngjune L. Gwon and Hsiang-TsungKung " Competing Cognitive Resilient Networks", IEEE Transactions on Cognitive Communications and Networking ,Volume: 2, Issue: 1, 2016 .
  2. Brandon F. Lo and Ian F. Akyildiz"Multiagent Jamming-Resilient Control Channel Game for Cognitive Radio Ad Hoc Networks",IEEE International Conference on Communication,2012.
  3. AmrutaP.Chaudhary, SharvariS.Tantarpale, Badnera, Amravati India, SeemaH.Rajput, Kondhwa "A Novel Approach to Secure Cognitive Radio Network using Dynamic Quiet Period Scheduling for Detection Of Control Channel Jamming Attack"in International Journal of Innovative and Emerging Research in Engineering Volume 3, Special Issue 1, ICSTSD,2016.
  4. FetenSlimeni, Bart Scheers, ZiedChtourou and Vincent Le Nir "Jamming mitigation in cognitive radio networks using a modified Q-learning algorithm" IEEE Conference Publications,2015.
  5. Changlong Chen and Min Song, University of Toledo ChunSheng Xin "A Game-Theoretical Anti-Jamming Scheme for Cognitive Radio Networks"IEEE Networks, Volume: 27, Issue: 3, 2013.
  6. [Karin Sallhammar, Bjarne E. Helvik, Svein J. Knapskog Centre for Quantifiable Quality of Service " On Stochastic Modeling for Integrated Security and Dependability Evaluation" in IEEE Conference Publications,2006
  7. C Manogna.KNaik, "Detection of Jamming attack in Cognitive Radio Networks",International Journal of Recent Advances in Engineering & Technology (IJRAET),2014.
  8. Balogun ,A. Krings "On The Impact of Jamming Attacks on Cooperative Spectrum Sensing in Cognitive Radio Networks" ACM,2013.
  9. Ian F. Akyildiz , Brandon F. Lo , Ravikumar Balakrishnan, Cooperative spectrum sensing in cognitive radio networks: A survey, Physical Communication, v.4 n.1,March, 2011, p.40-62, https://doi.org/10.1016/j.phycom.2010.12.003
  10. Asterjadhi, M. Zorzi, JENNA: A Jamming Evasive Network coding Neighbor discovery Algorithm for Cognitive Radio Networks, In IEEE ICC Workshop on Cooperative and Cognitive Mobile Networks (CoCoNet3), Cape Town, South Africa, May, 2010
  11. KresimirDabcevic,AlejandroBetancourt,LucioMarcenaro and Carlo S Regazzoni"Intelligent cognitive radio jamming-a game - theoretical approach",Springer,2014.
  12. FetenSlimenia, Bart Scheersb, ZiedChtouroua, Vincent Le Nirb, RabahAttia "Cognitive Radio Jamming Mitigation using Markov Decision Process and Reinforcement Learning",The International Conference on Advanced Wireless, Information, and Communication Technologies,2015.
  13. B. F. Lo"A survey of common control channel design in cognitive radio networks",IEEE Physical Communication,2011.
  14. I. F. Akyildiz, W.-Y. Lee, and K. R. Chowdhury "Crahns: Cognitive radio ad hoc networks,"AD hoc networks,2009.
  15. A. Masri, C.-F. Chiasserini, and A. Perotti "Control information exchange through uwb in cognitive radio networks," in International Symposium on Wireless Pervasive Computing (ISWPC),2010.
  16. D. L. Wasden, H. Moradi, and B. Farhang-Boroujeny "Design and implementation of an underlay control channel for cognitive radios"inIEEE Journal on Selected Areas in Communications,2012.
  17. J. Hu M. P. Wellman "Nash q-learning for general-sum stochastic games" ,Journal of Machine Learning Research,vol. 4 no. 6, 2003, pp. 1039-1069.
  18. M. Bowling M. Veloso "M (2002) multiagent learning using a variable learning rate" ,Artificial Intelligence,vol. 136, no. 2, 2002, pp. 215-250. https://doi.org/10.1016/S0004-3702(02)00121-2
  19. S. Abdallah V. Lesser "A multiagent reinforcement learning algorithm with non-linear dynamics" ,Journal of Artificial Intelligence Research,vol. 33 no. 3, 2014, pp. 521-549.
  20. Albert H. R. Ko R. Sabourin F. Gagnon "Performance of distributed multi-agent multi-state reinforcement spectrum management using different exploration schemes" ,Expert Systems with Applications An International Journal,vol. 40 no. 10, 2013, pp. 4115-4126.. https://doi.org/10.1016/j.eswa.2013.01.035
  21. K. Bian, J.-M. Park, and R. Chen "A quorum-based framework for establishing control channels in dynamic spectrum access networks,"Proceedings of the 15th annual international conference on Mobile computing and networking, ACM,2006
  22. J. Zhao, H. Zheng, and G.-H. Yang "Distributed coordination in dynamic spectrum allocation networks," First IEEE InternationalSymposium on New Frontiers in Dynamic Spectrum Access Networks, IEEE,2007
  23. R.S. Sutton A.G. Barto," Introduction to Reinforcement Learning", MIT Press, 1998.
  24. P.Vidhyarani ,S. Vimal,"Defense against Routing towards Primary User attack in Cognitive Radio Networks", TCIFES 2015- International Conference on Technological Convergence For Information, Food, Health and Energy Security- 1 & 2 May 2015.
  25. S.Vimal,L.Kalaivani,"Secure Authentication mechanisms to mitigate PUE attacks for mobile Cognitive Radio networks" IEEE,IEEE CSI,IEEE-ITS & BRNS sponsored International Conference on Computing Technologies and Intelligent Data Engineering, 7-9 Jan 2016.
  26. M Kaliappan, B Paramasivan,"Enhancing secure routing in mobile ad hoc networks using a dynamic bayesiansignalling game model",Computers& Electrical Engineering 41, 301-313,2015. https://doi.org/10.1016/j.compeleceng.2014.11.011
  27. Q. Mahmoud, Cognitive Networks: Towards Self-Aware Networks. John Wiley and Sons, 2007. .
  28. Alhakami, A. Mansour, and G. A. Safdar, "Spectrum Sharing Security and Attacks in CRNs: a Review," International Journal of Advanced Computer Science and Applications(IJACSA), vol. 5, no. 1, 2014, pp. 76-87.
  29. Vimal, S., Kalaivani, L. &Kaliappan, Collaborative approach on mitigating spectrum sensing data hijack attack and dynamic spectrum allocation based on CASG modeling in wireless cognitive radio networks, M. Cluster Computing (2017),https://doi.org/10.1007/s10586-017-1092-0
  30. A.C. Talay D.T. Altilar "Self Adaptive Routing for Dynamic Spectrum Access in Cognitive Radio Networks" ,J. Network Computer Applications,vol. 36 no. 4,2013, pp. 1140-51. https://doi.org/10.1016/j.jnca.2013.01.007
  31. J.Y. Yu P.H.J. Chong "A Survey of Clustering Schemes for Mobile Ad Hoc Networks" ,IEEE Commun. Surveys &Tutorials,vol. 7, no. 1, 2005, pp. 32-48. https://doi.org/10.1109/COMST.2005.1423333
  32. K.-L.A. Yau et al. "Clustering Algorithms for Cognitive Radio Networks: A Survey" ,J. Network Computer Applications,vol. 45, 2014, pp. 79-95. https://doi.org/10.1016/j.jnca.2014.07.020
  33. M. Youssef et al. "Routing Metrics of Cognitive Radio Networks: A Survey" ,IEEECommun. Surveys &Tutorials,vol. 16, no. 1, 2014, pp. 92-109 ,. https://doi.org/10.1109/SURV.2013.082713.00184
  34. X. Chen B. Fu Y. He M. Wu "Timesharing-tracking framework for decentralized reinforcement learning in fully cooperative multi-agent system" ,AutomaticaSinica IEEE/CAA Journal of,vol. 1 no. 2 April 2014, pp. 127-133. https://doi.org/10.1109/JAS.2014.7004541
  35. M. Bowling M. Veloso "M (2002) multiagent learning using a variable learning rate",ArtificialIntelligence,vol. 136 no. 2 2002, pp. 215-250.
  36. Q. Zhao L. Tong A. Swami Y. Chen "Decentralized cognitive mac for opportunistic spectrum access in ad hoc networks: A pomdp frame-work" ,Selected Areas in Communications IEEE Journal on,vol. 25 no. 3, ,April2007, pp.589-600. https://doi.org/10.1109/JSAC.2007.070409
  37. Dr.E.Mariappan,et.al.," Energy Efficient Routing protocol using Grover's Searching algorithm using MANET" Asian Journal of Information Technology,Vol:15,issue:24,2016.
  38. P.Subbulakshmi,et.al.,,"Secure data packet transmission in manet using enhanced identity-based cryptography (eibc)".International Journal of New Technologies in Science and Engineering Vol. 3, Issue 12, Pg:35-42,Dec 2016.
  39. Subbulakshmi,p.,et.al., "Honest Auction Based Spectrum Assignment and Exploiting Spectrum Sensing Data Falsification Attack Using Stochastic Game Theory in Wireless Cognitive Radio Network", Wireless Personal Communication (2017). https://doi.org/10.1007/s11277-017-5105-3.
  40. Vimal, S., Kalaivani, L.,Kaliappan,M.,Suresh,A., Xiao-Zhi Gao, Varatharajan ,R., Development of secured data transmission using machine learning based discrete time partial observed markov model and energy optimization in Cognitive radio networks, Neural Comput&Applic (2018), DOI: 10.1007/s00521-018-3788-3.
  41. Madhumitha Ramamurthy, Y. Harold Robinson, S. Vimal, A. Suresh,"Auto encoder based dimensionality reduction and classification using convolutional neural networks for hyperspectral images,Microprocessors and Microsystems, Volume 79,2020,103280,ISSN 0141-9331,https://doi.org/10.1016/j.micpro.2020.103280.