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Abnormal Information Identification and Elimination in Cognitive Networks

Volume 14, Number 10, October 2018, pp. 2271-2279
DOI: 10.23940/ijpe.18.10.p3.22712279

Ruowu Wua, Xiang Chena, Hui Hana, Haojun Zhaob, and Yun Linb

aState Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System, Luoyang, 471003, China
bHarbin Engineering University, Harbin, 150001, China

(Submitted on May 17, 2018; Revised on July 8, 2018; Accepted on August 17, 2018)

Abstract:

The electromagnetic spectrum is an important national strategic resource, and spectrum sensing data falsification (SSDF) is an attack method that destroys the cognitive network and makes it impossible to be used effectively. Malicious users capture the sensory nodes through cyber attacks, virus intrusions, etc., tampering with the perceived data and making the cognitive network biased or even completely reversed. In order to eliminate the negative effects caused by the identification and elimination of abnormal information in the electromagnetic spectrum in multi-user collaboration and to ensure the desired effect, this paper studies and constructs a robust cognitive user evaluation reference system based on improving the performance of cooperative spectrum sensing. The impact of attack behavior on the reference frame is greatly reduced. At the same time, the attacker’s identification and elimination algorithm are improved, and the influence of abnormal data on the perceived performance under the combined effect is eliminated.

 

References: 20

                1. R. Benjamin, “Security Considerations in Communications Systems and Networks,” Communications Speech and Vision Iee Proceedings I, Vol. 137, No. 2, pp. 61-72, April 1990
                2. H. Li and Z. Han, “Catch Me If You Can: An Abnormality Detection Approach for Collaborative Spectrum Sensing in Cognitive Radio Networks,” IEEE Transactions on Wireless Communications, Vol. 9, No. 11, pp. 3554-3565, November 2010
                3. S. Rawat, P. Anand, H. Chen, and P. K. Varshney, “Collaborative Spectrum Sensing in the Presence of Byzantine Attacks in Cognitive Radio Networks,” IEEE Transactions on Signal Processing, Vol. 59, No. 2, pp. 774-786, February 2011
                4. K. Zeng, P. Paweczak, and D. Cabri, “Reputation-based Cooperative Spectrum Sensing with Trusted Nodes Assistance,” IEEE Communications Letters, Vol. 14, No. 3, pp. 226-228, March 2010
                5. C. Chen, M. Song, C. S. Xin, and M. Alam, “A Robust Malicious User Detection Scheme in Cooperative Spectrum Sensing,” in Proceedings of 2012 IEEE Global Communications Conference, pp.4856-4861, 2012
                6. F. Penna, Y. Sun, L. Dolecek, and D. Cabric, “Detecting and Counteracting Statistical Attacks in Cooperative Spectrum Sensing,” IEEE Transactions on Signal Processing, Vol. 60, No. 4, pp. 1806-1822, April 2012
                7. G. Noh, S. Lim, S. Lee, and D. Hong, “Goodness-of-Fit-based Malicious User Detection in Cooperative Spectrum Sensing,” in Proceedings of IEEE 76th Vehicular Technology Conference, pp. 1-5, 2012
                8. S. Liu, H. J. Zhu, S. Li, X. Li, C. L. Chen, and X. P. Guan, “An Adaptive Deviation-Tolerant Secure Scheme for Distributed Cooperative Spectrum Sensing,” in Proceedings of 2012 IEEE Global Communication Conference, pp. 603-608, 2012
                9. T. Zhang, R. Safavi-Naini, and Z. Li, “ReDiSen: Reputation-based Secure Cooperative Sensing in Distributed Cognitive Radio Networks,” in Proceedings of IEEE International Conference on Communication, pp. 2601-2605, 2013
                10. C. Chen, M. Song, C. S. Xin, and M. Alam, “A Robust Malicious User Detection Scheme in Cooperative Spectrum Sensing,” in Proceedings of IEEE Global Communication Conference, pp. 4856-4861, 2012
                11. S. Bhattacharjee, S. Debroy, M. Chatterjee, and K. Kwiat, “Utilizing Misleading Information for Cooperative Spectrum Sensing in Cognitive Radio Networks,” in Proceedings of IEEE International Conference on Communications, pp. 2612-2616, 2013
                12. R. Chen, J. M. Park, and K. Bian, “Robust Distributed Spectrum Sensing in Cognitive Radio Networks,” INFOCOM, Phoenix, AZ, 13-18 April 2008
                13. K. Zeng, P. Paweczak, and D. Cabri, “Reputation-based Cooperative Spectrum Sensing with Trusted Nodes Assistance,” IEEE Communication Letters, Vol. 14, No. 3, pp. 226-228, March 2010
                14. X. He, H. Dai, and P. Ning, “A Byzantine Attack Defender in Cognitive Radio Networks: The Conditional Frequency Check,” IEEE Transactions on Wireless Communication, Vol. 12, No. 5, pp. 2512-2523, May 2013
                15. G. Noh, S. Lim, S. Lee, and D. Hong, “Goodness-of-Fit-based Malicious User Detection in Cooperative Spectrum Sensing,” in Proceedings of IEEE 76th Vehicular Technology Conference, pp. 1-5, 2012
                16. S. Althunibat, B. J. Denise, and F. Granelli, “Identification and Punishment Policies for Spectrum Sensing Data Falsification Attackers Using Delivery-based Assessment,” IEEE Transactions on Vehicular Technology, Vol. 65, No. 9, pp. 7308-7321, September 2016
                17. Y. Zhao, M. Song, and C. Xin, “A Weighted Cooperative Spectrum Sensing Framework for Infrastructure-based Cognitive Radio Networks,” Computer Communication, Vol. 2011, No. 34, pp. 1510-1517, February 2011
                18. M. Jo, L. Han, and D. Kim, “Selfish Attacks and Detection in Cognitive Radio Ad-Hoc Networks,” IEEE Network, Vol. 27, No. 3, pp. 46-50, May-June 2013
                19. Y. Tu, Y. Lin, J. Wang, et al., “Semi-Supervised Learning with Generative Adversarial Networks on Digital Signal Modulation Classification,” CMC-Computers Materials & Continua, Vol. 55, No. 2, pp. 243-254, 2018
                20. T. Bin, T. Ya, Z. Shaoyue, and L. Yun, “Digital Signal Modulation Classification with Data Augmentation Using Generative Adversarial Nets in Cognitive Radio Networks,” IEEE Access, Vol. 6, pp. 15713-15722, 2018

                               

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