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Electronics and Telecommunications Research Institute (한국전자통신연구원)
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Sequential fusion to defend against sensing data falsification attack for cognitive Internet of Things

  • Wu, Jun (School of Communication Engineering, Hangzhou Dianzi University) ;
  • Wang, Cong (School of Information Science and Engineering, Southeast University) ;
  • Yu, Yue (School of Information Science and Engineering, Southeast University) ;
  • Song, Tiecheng (School of Information Science and Engineering, Southeast University) ;
  • Hu, Jing (School of Information Science and Engineering, Southeast University)
  • Received : 2019.08.21
  • Accepted : 2019.11.14
  • Published : 2020.12.14
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Abstract

Internet of Things (IoT) is considered the future network to support wireless communications. To realize an IoT network, sufficient spectrum should be allocated for the rapidly increasing IoT devices. Through cognitive radio, unlicensed IoT devices exploit cooperative spectrum sensing (CSS) to opportunistically access a licensed spectrum without causing harmful interference to licensed primary users (PUs), thereby effectively improving the spectrum utilization. However, an open access cognitive IoT allows abnormal IoT devices to undermine the CSS process. Herein, we first establish a hard-combining attack model according to the malicious behavior of falsifying sensing data. Subsequently, we propose a weighted sequential hypothesis test (WSHT) to increase the PU detection accuracy and decrease the sampling number, which comprises the data transmission status-trust evaluation mechanism, sensing data availability, and sequential hypothesis test. Finally, simulation results show that when various attacks are encountered, the requirements of the WSHT are less than those of the conventional WSHT for a better detection performance.

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References

  1. K. Ashton, That 'internet of things' thing, RFID J. 22 (2009), no. 7, 97-114.
  2. L. Atzori, A. Iera, and G. Morabito, The internet of things: a survey, Computer Netw. 54 (2010), no. 15, 2787-2805. https://doi.org/10.1016/j.comnet.2010.05.010
  3. W. Lu et al., Incentive mechanism based cooperative spectrum sharing for OFDM cognitive IoT network, IEEE Trans. Netw. Sci. Engin. (2019). https://doi.org/10.1109/TNSE.2019.2917071
  4. M. Zhang et al., Cognitive internet of things: Concepts and application example, Int. J. Comput. Sci. Issues 9 (2012), no. 6, 151.
  5. Q. Wu et al., Cognitive internet of things: a new paradigm beyond connection, IEEE Internet Things J. 1 (2014), no. 2, 129-143. https://doi.org/10.1109/JIOT.2014.2311513
  6. J. Ploennigs, A. Ba, and M. Barry, Materializing the promises of cognitive IoT: How cognitive buildings are shaping the way, IEEE Internet Things J. 5 (2017), no. 4, 2367-2374. https://doi.org/10.1109/jiot.2017.2755376
  7. K. Katzis and H. Ahmadi, Challenges implementing Internet of Things (IoT) using cognitive radio capabilities in 5G mobile networks, Internet of Things (IoT) in 5G Mobile Technologies. Springer, Cham, 2016, pp. 55-76.
  8. A. A. Khan, M. H. Rehmani, and A. Rachedi, When cognitive radio meets the internet of things? in Proc. IEEE Int. Wirel. Commun. Mob. Comput. Conf. (Paphos, Cyprus), Sept. 2016, pp. 469-474.
  9. A. A. Khan, M. H. Rehmani, and A. Rachedi, Cognitive-radiobased internet of things: applications, architectures, spectrum related functionalities, and future research directions, IEEE Wirel. Commun. 24 (2017), no. 3, 17-25. https://doi.org/10.1109/MWC.2017.1600404
  10. S. Kim, New game paradigm for IoT systems, in Game Theory: Breakthroughs in Research and Practice. Hershey, PA, USA: IGI Global, 2017, p. 120.
  11. K. Zaheer et al., A Survey of decision-theoretic models for cognitive internet of things (CIoT), IEEE Access 6 (2018), 22489-22512. https://doi.org/10.1109/access.2018.2825282
  12. A. N. Akhtar, F. Arif, and A. M. Siddique, Spectrum decision framework to support cognitive radio based IoT in 5G, Cognitive Radio in 4G/5G Wirel, Commun. Syst. (2018), 73.
  13. P. Y. Chen, S. M. Cheng, and K. C. Chen, Information fusion to defend intentional attack in internet of things, IEEE Internet Things J. 1 (2014), no. 4, 337-348. https://doi.org/10.1109/JIOT.2014.2337018
  14. K. Zhang et al., Sybil attacks and their defenses in the internet of things, IEEE Internet Things J. 1 (2014), no. 5, 372-383. https://doi.org/10.1109/JIOT.2014.2344013
  15. Z. Li et al., Worst-case cooperative jamming for secure communications in CIoT networks, Sens. 16 (2016), no. 3, 339. https://doi.org/10.3390/s16030339
  16. H. A. B. Salameh et al., Spectrum assignment in cognitive radio networks for internet-of-things delay-sensitive applications under jamming attacks, IEEE Internet Things J. 5 (2018), no. 3, 1904-1913. https://doi.org/10.1109/jiot.2018.2817339
  17. S. C. Lin, C. Y. Wen, and W. A. Sethares, Two-tier device-based authentication protocol against PUEA attacks for IoT applications, IEEE Trans. Signal Inf. Process. over Netw. 4 (2017), no. 1, 33-47. https://doi.org/10.1109/tsipn.2017.2723761
  18. H. Suo et al., Security in the internet of things: a review, in Proc. IEEE Int. Conf. Comput. Sci. Electron. Eng. (Hangzhou, China), Mar. 2012, pp. 648-651.
  19. R. Roman, J. Zhou, and J. Lopez, On the features and challenges of security and privacy in distributed internet of things, Comput. Netw. 57 (2013), no. 10, 2266-2279. https://doi.org/10.1016/j.comnet.2012.12.018
  20. L. Da Xu, W. He, and S. Li, Internet of things in industries: a survey, IEEE Trans. Ind. Inform. 10 (2014), no. 4, 2233-2243. https://doi.org/10.1109/TII.2014.2300753
  21. M. Abomhara and G. M. Koien, Security and privacy in the Internet of Things: Current status and open issues, in Proc. Int. Conf. Priv. Secur. Mob. Syst. (Aalborg, Denmark), May 2014, pp. 1-8.
  22. Q. Jing et al., Security of the Internet of Things: perspectives and challenges, Wirel. Netw. 20 (2014), no. 8, 2481-2501. https://doi.org/10.1007/s11276-014-0761-7
  23. I. Andrea, C. Chrysostomou, and G. Hadjichristofi, Internet of Things: Security vulnerabilities and challenges, in Proc. IEEE Symp. Comput. Commun. (Larnaca, Cyprus), July 2015, pp. 180-187.
  24. S. Sicari et al., Security, privacy and trust in internet of things: The road ahead, Comp. Netw. 76 (2015), 146-164. https://doi.org/10.1016/j.comnet.2014.11.008
  25. W. Wei, A. T. Yang, and W. Shi, Security in internet of things: Opportunities and challenges, in Proc. Int. Conf. Identif. Inf. Knowl. Internet Things (Beijing, China), Oct. 2016, pp. 512-518.
  26. I. Yaqoob et al., Internet of things architecture: recent advances, taxonomy, requirements, and open challenges, IEEE Wirel. Commun. 24 (2017), no. 3, 10-16. https://doi.org/10.1109/MWC.2017.1600421
  27. F. A. Alaba et al., Internet of things security: a survey, J. Netw. Comput. Appl. 88 (2017), 10-28. https://doi.org/10.1016/j.jnca.2017.04.002
  28. K. Sha et al., On security challenges and open issues in internet of things, Future Gener. Comput. Syst. 83 (2018), 326-337. https://doi.org/10.1016/j.future.2018.01.059
  29. J. E. Siegel, S. Kumar, and S. E. Sarma, The future internet of things: Secure, efficient, and model-based, IEEE Internet Things J. 5 (2017), no. 4, 2386-2398. https://doi.org/10.1109/jiot.2017.2755620
  30. Y. Chen, S. Kar, and J. M. F. Moura, The internet of things: secure distributed inference, IEEE Signal Process. Mag. 35 (2018), no. 5, 64-75. https://doi.org/10.1109/msp.2018.2842097
  31. H. A. Khattak et al., Perception layer security in Internet of Things, Future Gener. Comput. Syst. 100 (2019), 144-164. https://doi.org/10.1016/j.future.2019.04.038
  32. R. Chen, J. M. J. Park, and K. Bian, Robustness against Byzantine failures in distributed spectrum sensing, Comput. Commun. 35 (2012), no. 17, 2115-2124. https://doi.org/10.1016/j.comcom.2012.07.014
  33. C. Y. Chen et al., Secure centralized spectrum sensing for cognitive radio networks, Wirel. Netw. 18 (2012), no. 6, 667-677. https://doi.org/10.1007/s11276-012-0426-3
  34. H. Wang et al. An improved spectrum sensing data-fusion algorithm based on reputation, in Proc. Int. Conf. Commun. Signal Process., Syst. Springer, Cham, 2015, pp. 359-364.
  35. J. Lu and P. Wei, Improved cooperative spectrum sensing based on the reputation in cognitive radio networks, Int. J. Electron. 102 (2015), no. 5, 855-863. https://doi.org/10.1080/00207217.2014.942887
  36. A. A. Sharifi and J. M. Niya, Securing collaborative spectrum sensing against malicious attackers in cognitive radio networks, Wirel. Pers. Commun. 90 (2016), no. 1, 75-91. https://doi.org/10.1007/s11277-016-3331-8
  37. F. Zhao and J. Feng, Supporting trusted soft decision scheme using volatility decay in cooperative spectrum sensing, KSII Trans. Internet and Inf. Syst. 10 (2016), no. 5, 2067-2080. https://doi.org/10.3837/tiis.2016.05.007
  38. H. Alizadeh et al., Attack-aware cooperative spectrum sensing in cognitive radio networks under Byzantine attack, J. Commun. Engin. 6 (2017), no. 1, 81-98.
  39. B. Kailkhura et al., Distributed Bayesian detection in the presence of Byzantine data, IEEE Trans. Signal Process. 63 (2015), no. 19, 5250-5263. https://doi.org/10.1109/TSP.2015.2450191
  40. P. K. Varshney, Distributed detection and data fusion, Berlin, Germany: Springer-Verla, 1997.

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