Detecting Byzantine attack in cognitive radio networks using machine learning
Author:
Publisher
Springer Science and Business Media LLC
Subject
Electrical and Electronic Engineering,Computer Networks and Communications,Information Systems
Link
https://link.springer.com/content/pdf/10.1007/s11276-020-02398-w.pdf
Reference37 articles.
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2. Zhang, L., Ding, G., Wu, Q., Zou, Y., Han, Z., & Wang, J. (2015). Byzantine attack and defense in cognitive radio networks: A survey. IEEE Communications Surveys and Tutorials, 17(3), 1342–1363.
3. Attar, A., Tang, H., Vasilakos, A. V., Yu, F. R., & Leung, V. C. M. (2012). A survey of security challenges in cognitive radio networks: Solutions and future research directions. Proceedings of the IEEE, 100(12), 3172–3186.
4. Cheng, Z., Song, T., Zhang, J., Hu, J., Hu, Y., Shen, L., Li, X., & Wu, J. (2017). Self-organizing map-based scheme against probabilistic SSDF attack in cognitive radio networks. In Proceedings of IEEE WCSP, 978-1-5386-2062-5.
5. Marchang, N., Taggu, A., & Patra, A. K. (2018). Detecting Byzantine attack in cognitive radio networks by exploiting frequency and ordering properties. IEEE Transactions on Coginitive Communications and Networking, 4(4), 816–824.
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