Abstract
Cooperative spectrum sensing allows users to detect available spectrum and utilize it. However, it is known that a few users can easily affect the cooperative decision at the fusion center by reporting falsified sensing data. The problem of detecting malicious users in cooperative spectrum sensing has been studied by numerous researchers. The basic approach is to compute the credibility of the reported data of each user and declare those with low credibility (below some threshold) as malicious. The computation of credibility can be based on several things such as pattern of historic behavior, entropy of the reported data, signal-to-noise ratio, etc. The credibility of the reported data of some user node can be expressed as an attack probability of that node. A node with higher attack probability will have lower credibility while a node with lower attack probability a higher credibility. Then, the problem of computing credibility of a node becomes the problem of computing attack probability of a node given a set of reported data. We can enumerate a list of all possible attack probability vectors for a set of nodes and the list of all possible energy level vectors of the channel for the observed time period, and compute which combination of attack probability and channel energy level vector can have the maximum probability to produce the reported channel energy level. However, the search space is quite large and grows exponentially as the number of user nodes and the number of time slots to observe increase. In this paper, we suggest algorithms that reduce the search space considerably and detect malicious users in linear time instead of exponential time. The suggested algorithms have been implemented and show promising results.
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Notes
In actual cooperative sensing network, we have one more sign which is zero. That is each \(r_{k}\) can be positive, negative, or zero. In this case, the number of pr's is \(3^{K}\) for K users. However, to simplify the explanation, we assume there is no \(r_{k}\) whose value is zero.
We use Y[i][k] instead of \(y_{k}^{i}\) to express the algorithm in more clean way. Both of them represent the channel energy level reported by user k at time i.
There are 8 different pr's, so each pr covers roughly one eighth of the search space. Therefore each pr represents \(\frac{{11^{3} *1}}{8}\) cells for each h vector. Only 3 pr's and 5 h vectors have survived after the reduction. Therefore the size of the reduced search space is roughly 5*\(\frac{{11^{3} *3}}{8}\).
References
Akyildiz, I. F., Brandon, F. R., & Balakrishnan, R. (2011). Cooperative spectrum sensing in cognitive radio networks: A survey. Physical Communication, 4, 40–62.
Chen, R., Park, J. M., & Bian, K. (2008). Robust distributed spectrum sensing in cognitive radio networks. In Proceedings of IEEE INFOCOM, (pp. 1876–1884).
Chen, C., Song, M., Xin, C., & Alam, M. (2012). A robust malicious user detection scheme in cooperative spectrum sensing. In IEEE GLOBECOM.
Chen, H., Zhou, M., Xie, L., & Li, J. (2017). Cooperative spectrum sensing with M-ary quantized data in cognitive radio networks under SSDF attacks. IEEE Transactions on Wireless Communications. https://doi.org/10.1109/TWC.2017.2707407.
Du, H., Fu, S., & Chu, H. (2015). A credibility-based defense ssdf attacks scheme for the expulsion of malicious users in cognitive radio. International Journal of Hybrid Information Technology, 8, 9.
Fatemieh, O., Farhadi, A., Chandra, R., & Gunter, C. A. (2011). Using classification to protect the integrity of spectrum measurements in white space networks. Network & Distributed Systems Security Symposium (NDSS), San Diego, CA, Feb 2011.
Kaligineedi, P., Khabbazian, M., & Bhargava, V. (2008). Secure cooperative sensing techniques for cognitive radio systems. In Proceedings of IEEE International Conference on Communications, ICC, (pp. 3406–3410).
Kaligineedi, P., Khabbazian, M., & Bhargava, V. (2010). Malicious user detection in a cognitive radio cooperative sensing system. IEEE Transactions on Wireless Communications, 9(8), 2488–2497.
Khan, M. S., Jibran, M., Koo, I., Kim, S. M., & Kim, J. (2019). A Double Adaptive Approach to Tackle Malicious Users in Cognitive Radio Networks. Wireless Communications and Mobile Computing, 2019, 2350694. https://doi.org/10.1155/2019/2350694.
Li, H., & Han, Z. (2010). Catch me if you can: An abnormality detection approach for collaborative spectrum sensing in cognitive radio networks. IEEE Transactions on Wireless Communications, 9(11), 3554–3565. https://doi.org/10.1109/TWC.2010.091510.100315.
Luo, Z., Zhao, S., Lu, Z., Xu, J., & Sagduyu, Y. E. (2019). When Attackers Meet AI: Learning-empowered Attacks in Cooperative Spectrum Sensing. arXiv:1905.01430v1.
Mishra, S., Sahai, A, & Brodersen, R. (2006). Cooperative sensing among cognitive radios. In Proceedings of IEEE ICC 2006, (vol. 4, pp. 1658–1663).
Nguyen-Thanh, N., & Koo, I. (2009). An enhanced cooperative spectrum sensing scheme based on evidence theory and reliability source evaluation in cognitive radio context. IEEE Communications Letters, 13(7), 492–494.
Peng, Q., Zeng, K., Jun, W., and Li, S. (2006). A distributed spectrum sensing scheme based on credibility and evidence theory. In Proceeding of the IEEE 17th International symposium on Personal, Indoor and Mobile Radio Communication, (pp. 1–5), Helsinki, Finland.
Peng, T., Chen, Y., Xiao, J., Zheng, Y., & Yang, J. (2016). Improved soft fusion-based cooperative spectrum sensing defense against SSDF attacks. In Proceedings of the 2016 International Conference on Computer, Information and Telecommunication Systems, CITS.
Penna, F., Sun, Y., Dolecek, L., & Cabric, D. (2012). Detecting and counteracting statistical attacks in cooperative spectrum sensing. IEEE Transactions on Signal Processing, 60(4), 1806–1822. https://doi.org/10.1109/TSP.2011.2181839.
Rajasegarar, S., Leckie, C., & Palaniswami, M. (2015). Pattern based anomalous user detection in cognitive radio networks. In IEEE ICASSP.
Thilina, K. M., Choi, K. W., Saquib, N., & Hossain, E. (2013). Machine learning techniques for cooperative spectrum sensing in cognitive radio networks. IEEE Journal on Selected Areas in Communications, 31(11), 2209–2221. https://doi.org/10.1109/JSAC.2013.131120
Wang, H., & Yao, Y. D. (2018). Primary user boundary detection in cognitive radio networks: Estimated secondary user locations and impact of malicious secondary users. IEEE Transactions on Vehicular Technology. https://doi.org/10.1109/TVT.2018.2796024
Ye, F., Zhang, X., & Li, Y. (2017). Collaborative spectrum sensing algorithm based on exponential entropy in cognitive radio networks. Symmetry, 9, 36.
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This work was supported by Inha University.
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Hwang, J., Kim, J., Sung, I. et al. Fast and Accurate Detection of Malicious Users in Cooperative Spectrum Sensing Network. Wireless Pers Commun 118, 1709–1731 (2021). https://doi.org/10.1007/s11277-021-08112-z
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DOI: https://doi.org/10.1007/s11277-021-08112-z