access icon free Identification as a deterrent for security enhancement in cognitive radio networks

Cognitive radio networks (CRNs) are prone to emerging coexistence security threats such as primary user emulation attack (PUEA). Specifically, a malicious CRN may mimic licensees' [primary users (PUs)] signal characteristics to force another CRN to vacate its channels thinking that PUs have returned. While existing schemes are promising to some extent on detecting PUEAs, they are not able to prevent the attacks. In this article, the authors propose a PUEA deterrent (PUED) algorithm that can provide PUEAs' commission details: offender CRNs and attacks' time and bandwidth. There are many similarities between PUED and closed-circuit television (CCTV) in terms of: deterrence strategy, reason for use, surveillance characteristics, surveillance outcome, and operation site. According to the criminology literature, robust CCTV systems have shown a significant reduction in visible offences (e.g. vehicle theft), reducing crime rates by 80%. Similarly, PUED will contribute the same effectiveness in deterring PUEAs. Furthermore, providing PUEAs' details will prevent the network's cognitive engine from considering the attacks as real PUs, consequently avoiding devising unreliable spectrum models for the attacked channels. Extensive simulations show the effectiveness of the PUED algorithm in terms of improving CRNs' performance.

Inspec keywords: cognitive radio; telecommunication security; closed circuit television

Other keywords: PUEA deterrent algorithm; closed-circuit television; primary user emulation attack; security enhancement; cognitive radio networks; CRN

Subjects: Radio links and equipment; Closed circuit television

References

    1. 1)
      • 28. Zhang, C., Yu, R., Zhang, Y.: ‘Performance analysis of primary user emulation attack in cognitive radio networks’. Proc. of Int. Wireless Communications and Mobile Computing Conf. (IWCMC), 2012, pp. 371376.
    2. 2)
      • 19. Doyle, A., Lippert, R., Lyon, D.: ‘Eyes everywhere, the global growth of camera surveillance’ (Routledge, New York, 2012, 1st edn.).
    3. 3)
      • 13. Xin, C.S., Song, M.: ‘Detection of PUE attacks in cognitive radio networks based on signal activity pattern’, IEEE Trans. Mobile Comput., 2014, 13, (5), pp. 10221034.
    4. 4)
      • 18. Fakhrudeen, A.M., Alani, O.Y.: ‘Reliable spectrum sharing management for cognitive radio networks’. Proc. of Wireless Innovation Forum Conf. on Wireless Communications Technology Software Defined Radio (Wlnn Comm ‘16), 2016, pp. 4957.
    5. 5)
      • 21. Johnson, S.D., Guerette, R.T., Bowers, K.: ‘Crime displacement: what we know, what we don't know, and what it means for crime reduction’, J. Exp. Criminol., 2014, 10, (4), pp. 549571.
    6. 6)
      • 17. Le, T.N., Chin, W.-L., Kao, W.-C.: ‘Cross-layer design for primary user emulation attacks detection in mobile cognitive radio networks’, IEEE Commun. Lett., 2015, 19, (5), pp. 799802.
    7. 7)
      • 8. Sharma, R.K., Rawat, D.B.: ‘Advances on security threats and countermeasures for cognitive radio networks: a survey’, IEEE Commun. Surv. Tut., 2016, 17, (02), pp. 10231043.
    8. 8)
      • 23. ‘Drag and drop countries around the map to compare their relative size’ [Online]. Available at http://www.thetruesize.com.
    9. 9)
      • 9. Jianwu, L., Zebing, F., Ping, Z.: ‘A survey of security issues in cognitive radio networks’, China Commun., 2015, 12, (3), pp. 132150.
    10. 10)
      • 20. Park, S.J.: ‘CCTV evaluation in Cincinnati within GIS environment for crime prevention’. MSc thesis, University of Cincinnati, Ohio, USA, 2013.
    11. 11)
      • 25. Haykin, S.: ‘Cognitive radio: brain-empowered wireless communications’, IEEE J. Sel. Areas Commun., 2005, 23, (02), pp. 201220.
    12. 12)
      • 26. ‘Big-O Notation’ [Online]. Available at http://interactivepython.org/runestone/static/pythonds/AlgorithmAnalysis/BigONotation.html.
    13. 13)
      • 24. Masonta, M.T., Mzyece, M., Ntlatlapa, N.: ‘Spectrum decision in cognitive radio networks: a survey’, IEEE Commun. Surv. Tut., 2013, 15, (03), pp. 10881107.
    14. 14)
      • 22. Rupp, M., Schwarz, S., Taranetz, M.: ‘The Vienna LTE-Advanced simulators, up and downlink, link and system level simulation’ (Springer, Singapore, 2016, 1st edn.).
    15. 15)
      • 11. Nguyen-Thanh, N., Ciblat, P., Pham, A.T., et al: ‘Surveillance strategies against primary user emulation attack in cognitive radio networks’, IEEE Trans. Wireless Commun., 2015, 14, (9), pp. 49814993.
    16. 16)
      • 5. Khan, A.A., Rehmani, M.H., Reisslein, M.: ‘Cognitive radio for smart grids: survey of architectures, spectrum sensing mechanisms, and networking protocols’, IEEE Commun. Surv. Tut., 2016, 18, (01), pp. 860898.
    17. 17)
      • 1. Chen, Y., Oh, H.-S.: ‘A survey of measurement-based spectrum occupancy modeling for cognitive radios’, IEEE Commun. Surv. Tut., 2016, 18, (01), pp. 848859.
    18. 18)
      • 12. Tan, Y., Sengupta, S., Subbalakshmi, K.: ‘Primary user emulation attack in dynamic spectrum access networks: a game-theoretic approach’, IET Commun.., 2012, 6, (8), pp. 964973.
    19. 19)
      • 27. Tyska, L.A., Fennelly, L.J.: ‘Physical security: 150 things you should know’ (Butterworth-Heinemann–Elsevier, Oxford, 2017, 2nd edn.).
    20. 20)
      • 2. Mitola, J., Maguire, G.Q.: ‘Cognitive radio: making software radios more personal’, IEEE Pers. Commun., 1999, 6, (4), pp. 1318.
    21. 21)
      • 10. Duc-Tuyen, T., Nguyen-Thanh, N., Ciblat, P., et al: ‘Extra-sensing game for malicious primary user emulator attack in cognitive radio network’. Proc. of European Conf. on Networks and Communications (EuCNC), 2015, pp. 306310.
    22. 22)
      • 7. Sun, H.J., Nallanathan, A., Wang, C.-X.: ‘Wideband spectrum sensing for cognitive radio networks: a survey’, IEEE Wireless Commun., 2013, 20, (2), pp. 7481.
    23. 23)
      • 16. Nguyen, N., Zheng, R., Han, Z.: ‘On identifying primary user emulation attacks in cognitive radio systems using nonparametric Bayesian classification’, IEEE Trans. Mobile Signal Process., 2014, 60, (3), pp. 14321445.
    24. 24)
      • 15. Yuan, Z., Niyato, D., Li, H., et al: ‘Defeating primary user emulation attacks using belief propagation in cognitive radio networks’, IEEE J. Sel. Areas Commun., 2012, 30, (10), pp. 18501860.
    25. 25)
      • 6. Cichon, K., Kliks, A., Bogucka, H.: ‘Energy-efficient cooperative spectrum sensing: a survey’, IEEE Commun. Surv. Tut., 2016, 18, (03), pp. 18611886.
    26. 26)
      • 3. Ahmed, E., Gani, A., Abolfazli, S., et al: ‘Channel assignment algorithms in cognitive radio networks: taxonomy, open issues, and challenges’, IEEE Commun. Surv. Tut., 2016, 18, (01), pp. 795823.
    27. 27)
      • 4. Goa, B., Park, J.-M., Yang, Y.: ‘A taxonomy of coexistence mechanisms for heterogeneous cognitive radio networks operating in TV white spaces’, IEEE Wireless Commun., 2012, 19, (4), pp. 4148.
    28. 28)
      • 14. Pu, D., Shi, Y., Ilyashenko, A.V., et al: ‘Detecting primary user emulation attack in cognitive radio networks’. Proc. of IEEE GLOBECOM, 2011, pp. 15.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-net.2017.0036
Loading

Related content

content/journals/10.1049/iet-net.2017.0036
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading