Machine Learning-Based Cooperative Clustering for Detecting and Mitigating Jamming Attacks in beyond 5G Networks
Author:
Funder
Korea Institute for Advancement of Technology
Institute of Information and Communication Technology Planning & Evaluation
National Research Foundation of Korea
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s10796-024-10534-6.pdf
Reference44 articles.
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3. Al-Shaihk, N. F. A., & Hassanpour, R. (2019). Active defense strategy against jamming attack in wireless sensor networks. International Journal of Computer Network and Information Security, 11, 1–13. https://doi.org/10.5815/ijcnis.2019.11.01
4. Arjoune, Y., & Faruque, S. (2020). Smart jamming attacks in 5g new radio: A review. In Proc. 2020 10th Annual Computing and Communication Workshop and Conference (CCWC) (pp. 1010–1015). Las Vegas, NV, USA. https://doi.org/10.1109/CCWC47524.2020.9031175
5. Bhavathankar, P., Misra, S., & Sarkar, S. (2017). Optimal decision rule-based ex-ante frequency hopping for jamming avoidance in wireless sensor networks. Computer Networks, 128, 172–185. https://doi.org/10.1016/j.comnet.2017.03.009
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