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Efficient attack strategy for legitimate energy-powered eavesdropping in tactical cognitive radio networks

  • Pham Duy Thanh
  • Tran Nhut Khai Hoan
  • Hiep Vu-Van
  • Insoo KooEmail author
Article
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Abstract

The cognitive radio network (CRN) is not only considered a useful medium for users, but it is also an environment vulnerable to proactive attackers. This paper studies an attack strategy for a legitimate energy-constrained eavesdropper (e.g., a government agency) to efficiently capture the suspicious wireless communications (i.e., an adversary communications link) in the physical layer of a CRN in tactical wireless networks. Since it is powered by an energy harvesting device, a full-duplex active eavesdropper constrained by a limited energy budget can simultaneously capture data and interfere with the suspicious cognitive transmissions to maximize the achievable wiretap rate while minimizing the suspicious transmission rate over a Rayleigh fading channel. The cognitive user operation is modeled in a time-slotted fashion. In this paper, we formulate the problem of maximizing a legitimate attack performance by adopting the framework of a partially observable Markov decision process. The decision is determined based on the remaining energy and a belief regarding the licensed channel activity in each time slot. Particularly, in each time slot, the eavesdropper can perform an optimal action based on two functional modes: (1) passive eavesdropping (overhearing data without jamming) or (2) active eavesdropping (overhearing data with the optimal amount of jamming energy) to maximize the long-term benefit. We illustrate the optimal policy and compare the performance of the proposed scheme with that of conventional schemes where the decision for the current time slot is only considered to maximize its immediate reward.

Keywords

Cognitive radio networks Physical layer Proactive eavesdropper Energy harvesting Jamming attack POMDP 

Notes

Acknowledgement

This work has supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (NRF-2018R1A2B6001714).

References

  1. 1.
    Hu, Z., Chen, Z., Liu, H., Shao, X., & Xing, G. (2013). System design for broadband digital radio broadcasting. IEEE Communications Magazine, 51(4), 58–65.CrossRefGoogle Scholar
  2. 2.
    Valenta, V., Maršálek, R., Baudoin, G., Villegas, M., Suarez, M., & Robert, F. (2010). Survey on spectrum utilization in Europe: Measurements, analyses and observations. In 2010 Proceedings of the fifth international conference on cognitive radio oriented wireless networks and communications (pp. 1–5). Cannes.Google Scholar
  3. 3.
    Federal Communications Commission-Spectrum Policy Task Force Group, Washington, DC, USA, Tech. Rep. ET Docket No. 02-135 (2002).Google Scholar
  4. 4.
    Zou, Y., Zhu, J., Wang, X., & Hanzo, L. (2016). A survey on wireless security: Technical challenges, recent advances, and future trends. Proceedings of the IEEE, 104(9), 1727–1765.CrossRefGoogle Scholar
  5. 5.
    Yang, N., Wang, L., Geraci, G., Elkashlan, M., Yuan, J., & Renzo, M. D. (2015). Safeguarding 5G wireless communication networks using physical layer security. IEEE Communications Magazine, 53(4), 20–27.CrossRefGoogle Scholar
  6. 6.
    Shiu, Y. S., Chang, S. Y., Wu, H. C., Huang, S. C. H., & Chen, H. H. (2011). Physical layer security in wireless networks: A tutorial. IEEE Wireless Communications, 18(2), 66–74.CrossRefGoogle Scholar
  7. 7.
    Buttyan, L., Gessner, D., Hessler, A., & Langendoerfer, P. (2010). Application of wireless sensor networks in critical infrastructure protection: Challenges and design options [Security and Privacy in Emerging Wireless Networks]. IEEE Wireless Communications, 17(5), 44–49.CrossRefGoogle Scholar
  8. 8.
    Mukherjee, A., Fakoorian, S. A. A., Huang, J., & Swindlehurst, A. L. (2014). Principles of physical layer security in multiuser wireless networks: A survey. IEEE Communications Surveys & Tutorials, 16(3), 1550–1573.CrossRefGoogle Scholar
  9. 9.
    Amariucai, G. T., & Wei, S. (2012). Half-duplex active eavesdropping in fast-fading channels: A Block-Markov Wyner secrecy encoding scheme. IEEE Transactions on Information Theory, 58(7), 4660–4677.MathSciNetCrossRefzbMATHGoogle Scholar
  10. 10.
    Mukherjee, A., & Swindlehurst, A. L. (2013). Jamming games in the MIMO wiretap channel with an active eavesdropper. IEEE Transactions on Signal Processing, 61(1), 82–91.MathSciNetCrossRefzbMATHGoogle Scholar
  11. 11.
    Basciftci, Y. O., Gungor, O., Koksal, C. E., & Ozguner, F. (2015). On the Secrecy Capacity of Block Fading Channels With a Hybrid Adversary. IEEE Transactions on Information Theory, 61(3), 1325–1343.MathSciNetCrossRefzbMATHGoogle Scholar
  12. 12.
    Feng, R., Dai, M., & Wang, H. (2017). Distributed beamforming in MISO SWIPT system. IEEE Transactions on Vehicular Technology, 66(6), 5440–5445.CrossRefGoogle Scholar
  13. 13.
    Wang, W., Teh, K. C., & Li, K. H. (2017). Artificial noise aided physical layer security in multi-antenna small-cell networks. IEEE Transactions on Information Forensics and Security, 12(6), 1470–1482.CrossRefGoogle Scholar
  14. 14.
    Li, Q., Yang, Y., Ma, W. K., Lin, M., Ge, J., & Lin, J. (2015). Robust cooperative beamforming and artificial noise design for physical-layer secrecy in af multi-antenna multi-relay networks. IEEE Transactions on Signal Processing, 63(1), 206–220.MathSciNetCrossRefzbMATHGoogle Scholar
  15. 15.
    Wang, D., Ren, P., Du, Q., Sun, L., & Wang, Y. (2017). Security provisioning for MISO vehicular relay networks via cooperative jamming and signal superposition. IEEE Transactions on Vehicular Technology, 66(12), 10732–10747.CrossRefGoogle Scholar
  16. 16.
    Tang, X., Ren, P., Wang, Y., Du, Q., & Sun, L. (2015). Securing wireless transmission against reactive jamming: A Stackelberg game framework. In 2015 IEEE global communications conference (GLOBECOM) (pp. 1–6). San Diego, CA.Google Scholar
  17. 17.
    Wang, Q., Xu, P., Ren, K., & Li, X. Y. (2012). Towards optimal adaptive UFH-based anti-jamming wireless communication. IEEE Journal on Selected Areas in Communications, 30(1), 16–30.CrossRefGoogle Scholar
  18. 18.
    D’Oro, S., Galluccio, L., Morabito, G., Palazzo, S., Chen, L., & Martignon, F. (2015). Defeating jamming with the power of silence: A game-theoretic analysis. IEEE Transactions on Wireless Communications, 14(5), 2337–2352.CrossRefGoogle Scholar
  19. 19.
    Tang, X., Ren, P., & Han, Z. (2017). Power-efficient secure transmission against full-duplex active eavesdropper: A game-theoretic framework. IEEE Access, 5, 24632–24645.CrossRefGoogle Scholar
  20. 20.
    Tang, X., Ren, P., & Han, Z. (2016). Combating full-duplex active eavesdropper: A game-theoretic perspective. In 2016 IEEE international conference on communications (ICC), Kuala Lumpur (pp. 1-6).Google Scholar
  21. 21.
    Chen, L., Zhu, Q., Meng, W., & Hua, Y. (2017). Fast power allocation for secure communication with full-duplex radio. IEEE Transactions on Signal Processing, 65(14), 3846–3861.MathSciNetCrossRefGoogle Scholar
  22. 22.
    Zheng, T. X., Wang, H. M., Yang, Q., & Lee, M. H. (2017). Safeguarding decentralized wireless networks using full-duplex jamming receivers. IEEE Transactions on Wireless Communications, 16(1), 278–292.CrossRefGoogle Scholar
  23. 23.
    Xu, J., Duan, L., & Zhang, R. (2016). Proactive eavesdropping via jamming for rate maximization over rayleigh fading channels. IEEE Wireless Communications Letters, 5(1), 80–83.CrossRefGoogle Scholar
  24. 24.
    Zhou, X., Maham, B., & Hjorungnes, A. (2012). Pilot contamination for active eavesdropping. IEEE Transactions on Wireless Communications, 11(3), 903–907.CrossRefGoogle Scholar
  25. 25.
    Nguyen, T. T., Vu-Van, H., & Koo, I. (2017). Data capture of cognitive radio-based red network by a blue network in tactical wireless networks. IEEE Sensors Journal, 17(1), 205–214.CrossRefGoogle Scholar
  26. 26.
    Chen, S., Zeng, K., & Mohapatra, P. (2014). Efficient data capturing for network forensics in cognitive radio networks. IEEE/ACM Transactions on Networking, 22(6), 1988–2000.CrossRefGoogle Scholar
  27. 27.
    Cao, Y., et al. (2018). Optimization or alignment: Secure primary transmission assisted by secondary networks. IEEE Journal on Selected Areas in Communications, 36(4), 905–917.CrossRefGoogle Scholar
  28. 28.
    Zhao, N., Yu, F. R., & Leung, V. C. M. (2015). Opportunistic communications in interference alignment networks with wireless power transfer. IEEE Wireless Communications, 22(1), 88–95.CrossRefGoogle Scholar
  29. 29.
    Zhao, N., Cao, Y., Yu, F. R., Chen, Y., Jin, M., & Leung, V. C. M. (2018). Artificial noise assisted secure interference networks with wireless power transfer. IEEE Transactions on Vehicular Technology, 67(2), 1087–1098.CrossRefGoogle Scholar
  30. 30.
    Zhao, N., Zhang, S., Yu, F. R., Chen, Y., Nallanathan, A., & Leung, V. C. M. (2017). Exploiting interference for energy harvesting: A survey, research issues, and challenges. IEEE Access, 5, 10403–10421.CrossRefGoogle Scholar
  31. 31.
    Bhowmick, A., Roy, S. D., & Kundu, S. (2015). Performance of secondary user with combined RF and non-RF based energy-harvesting in cognitive radio network. In 2015 IEEE international conference on advanced networks and telecommunications systems (ANTS), Kolkata (pp. 1–3).Google Scholar
  32. 32.
    Bhowmick, A., Yadav, K., Roy, S. D., & Kundu, S. (2017). Throughput of an energy harvesting cognitive radio network based on prediction of primary user. IEEE Transactions on Vehicular Technology, 66(9), 8119–8128.CrossRefGoogle Scholar
  33. 33.
    Zhai, C., Liu, J., & Zheng, L. (2016). Cooperative spectrum sharing with wireless energy harvesting in cognitive radio networks. IEEE Transactions on Vehicular Technology, 65(7), 5303–5316.CrossRefGoogle Scholar
  34. 34.
    Nhut Khai Hoan, T., & Koo, I. (2017). Multi-slot spectrum sensing schedule and transmitted energy allocation in harvested energy powered cognitive radio networks under secrecy constraints. IEEE Sensors Journal, 17(7), 2231–2240.CrossRefGoogle Scholar
  35. 35.
    Wang, Z., Chen, Z., Xia, B., Luo, L., & Zhou, J. (2016). Cognitive relay networks with energy harvesting and information transfer: Design, analysis, and optimization. IEEE Transactions on Wireless Communications, 15(4), 2562–2576.CrossRefGoogle Scholar
  36. 36.
    Gabry, F., Zappone, A., Thobaben, R., Jorswieck, E. A., & Skoglund, M. (2015). Energy efficiency analysis of cooperative jamming in cognitive radio networks with secrecy constraints. IEEE Wireless Communications Letters, 4(4), 437–440.CrossRefGoogle Scholar
  37. 37.
    Liang, Y. C., Zeng, Y., Peh, E. C. Y., & Hoang, A. T. (2008). Sensing-throughput tradeoff for cognitive radio networks. IEEE Transactions on Wireless Communications, 7(4), 1326–1337.CrossRefGoogle Scholar
  38. 38.
    Atapattu, S., Tellambura, C., & Jiang, H. (2011). Energy detection based cooperative spectrum sensing in cognitive radio networks. IEEE Transactions on Wireless Communications, 10(4), 1232–1241.CrossRefGoogle Scholar
  39. 39.
    Soltanmohammadi, E., & Naraghi-Pour, M. (2014). Fast detection of malicious behavior in cooperative spectrum sensing. IEEE Journal on Selected Areas in Communications, 32(3), 377–386.CrossRefGoogle Scholar
  40. 40.
    Stevenson, C. R., Chouinard, G., Lei, Z., Hu, W., Shellhammer, S. J., & Caldwell, W. (2009). IEEE 802.22: The first cognitive radio wireless regional area network standard. IEEE Communications Magazine, 47(1), 130–138.CrossRefGoogle Scholar
  41. 41.
    IEEE Standard for Information technology—Local and metropolitan area networks—Specific requirements—Part 22: Cognitive wireless RAN medium access control (MAC) and physical layer (PHY) specifications: Policies and procedures for operation in the TV Bands. In: IEEE Std 802.22-2011 (pp. 1-680), 1 July 2011.Google Scholar
  42. 42.
    Allam, S., Dufour, F., & Bertrand, P. (2001). Discrete-time estimation of a Markov chain with marked point process observations. Application to Markovian jump filtering. IEEE Transactions on Automatic Control, 46(6), 903–908.MathSciNetCrossRefzbMATHGoogle Scholar
  43. 43.
    Sultan, A. (2012). Sensing and transmit energy optimization for an energy harvesting cognitive radio. IEEE Wireless Communications Letters, 1(5), 500–503.CrossRefGoogle Scholar
  44. 44.
    Bertsekas, D. P. (2001). Dynamic programming and optimal control (2nd ed., Vol. 1-2). Belmont: Athena Scientic.zbMATHGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Pham Duy Thanh
    • 1
  • Tran Nhut Khai Hoan
    • 2
  • Hiep Vu-Van
    • 1
  • Insoo Koo
    • 1
    Email author
  1. 1.School of Electrical EngineeringUniversity of Ulsan (UOU)UlsanRepublic of Korea
  2. 2.Can Tho UniversityCan ThoVietnam

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