COMPARATIVE ANALYSIS OF IMPACTS OF VARIOUS JAMMING ATTACKS ON 5G NETWORK
Abstract
With the promise of faster data speeds and more dependable service, fifth-generation (5G) wireless cellular networks are encouraging the adoption of cutting-edge technologies like smart cities and the Internet-of-things (IoTs). However, 5G networks are susceptible to possible interference because of their open-sharing principles, especially from malicious jamming attacks. Notwithstanding, for further notable progress in 5G technology, thorough simulations and studies are imperative to properly comprehend the fundamentals of jamming attacks on 5G networks. To close this gap, this study simulated and analyzed jamming attacks on 5G communication systems to determine how these attacks affect important 5G performance indicators and assessed and suggested remedies that will maximize 5G network resilience against jamming.
References
Agiwal, M., Roy, A., & Saxena, N. (2016). Next generation 5G wireless networks: A comprehensive survey. IEEE communications surveys & tutorials, 18(3), 1617-1655. DOI: https://doi.org/10.1109/COMST.2016.2532458
Abhishek, N. V., & Gurusamy, M. (2021). JaDe: Low power jamming detection using machine learning in vehicular networks. IEEE Wireless Communications Letters, 10(10), 2210-2214. DOI: https://doi.org/10.1109/LWC.2021.3097162
Arjoune, Y., & Faruque, S. (2020, December). Real-time machine learning based on hoeffding decision trees for jamming detection in 5G new radio. In 2020 IEEE International Conference on Big Data (Big Data) (pp. 4988-4997). IEEE. DOI: https://doi.org/10.1109/BigData50022.2020.9377912
Arjoune, Y., & Faruque, S. (2020, January). Smart jamming attacks in 5G new radio: A DOI: https://doi.org/10.1109/CCWC47524.2020.9031175
review. In 2020 10th annual computing and communication workshop and conference (CCWC) (pp. 1010-1015). IEEE.
Birutis, M. A., & Mykkeltveit, A. (2022). Practical jamming of a commercial 5G radio system at 3.6 GHz. Procedia Computer Science, 205, 58-67. DOI: https://doi.org/10.1016/j.procs.2022.09.007
Bousalem, B., Sakka, M. A., Silva, V. F., Jaafar, W., Letaifa, A. B., & Langar, R. (2023, October). DDoS attacks mitigation in 5G-V2X networks: A reinforcement learning-based approach. In 2023 19th International Conference on Network and Service Management (CNSM) (pp. 1-5). IEEE. DOI: https://doi.org/10.23919/CNSM59352.2023.10327917
Do, T. T., Björnson, E., Larsson, E. G., & Razavizadeh, S. M. (2017). Jamming-resistant receivers for the massive MIMO uplink. IEEE Transactions on Information Forensics and Security, 13(1), 210-223. DOI: https://doi.org/10.1109/TIFS.2017.2746007
Grover, K., Lim, A., & Yang, Q. (2014). Jamming and anti–jamming techniques in wireless networks: a survey. International Journal of Ad Hoc and Ubiquitous Computing, 17(4), 197-215. DOI: https://doi.org/10.1504/IJAHUC.2014.066419
Gupta, A., & Jha, R. K. (2015). A survey of 5G network: Architecture and emerging technologies. IEEE access, 3, 1206-1232. DOI: https://doi.org/10.1109/ACCESS.2015.2461602
Haykin, S. (2008). Communication systems. John Wiley & Sons.
Isaac, S., Ayodeji, D. K., Luqman, Y., Karma, S. M., & Aminu, J. (2024). CYBER SECURITY ATTACK DETECTION MODEL USING SEMI-SUPERVISED LEARNING. FUDMA JOURNAL OF SCIENCES, 8(2), 92-100. DOI: https://doi.org/10.33003/fjs-2024-0802-2343
Karagiannis, D., & Argyriou, A. (2018). Jamming attack detection in a pair of RF communicating vehicles using unsupervised machine learning. vehicular communications, 13, 56-63. DOI: https://doi.org/10.1016/j.vehcom.2018.05.001
Kekirigoda, A., Hui, K. P., Cheng, Q., Lin, Z., Zhang, J. A., Nguyen, D. N., & Huang, X. (2019, November). Massive MIMO for tactical ad-hoc networks in RF contested environments. In MILCOM 2019-2019 IEEE Military Communications Conference (MILCOM) (pp. 658-663). IEEE. DOI: https://doi.org/10.1109/MILCOM47813.2019.9020756
Krayani, A., Barabino, G., Marcenaro, L., & Regazzoni, C. (2023, March). Integrated sensing and communication for joint gps spoofing and jamming detection in vehicular v2x networks. In 2023 IEEE Wireless Communications and Networking Conference (WCNC) (pp. 1-7). IEEE. DOI: https://doi.org/10.1109/WCNC55385.2023.10118852
Li, W., Chen, J., Liu, X., Wang, X., Li, Y., Liu, D., & Xu, Y. (2022). Intelligent dynamic spectrum anti-jamming communications: A deep reinforcement learning perspective. IEEE Wireless Communications, 29(5), 60-67. DOI: https://doi.org/10.1109/MWC.103.2100365
Lichtman, M., Rao, R., Marojevic, V., Reed, J., & Jover, R. P. (2018, May). 5G NR jamming, spoofing, and sniffing: Threat assessment and mitigation. In 2018 IEEE international conference on communications workshops (ICC Workshops) (pp. 1-6). IEEE. DOI: https://doi.org/10.1109/ICCW.2018.8403769
Pelechrinis, K., Iliofotou, M., & Krishnamurthy, S. V. (2010). Denial of service attacks in wireless networks: The case of jammers. IEEE Communications surveys & tutorials, 13(2), 245-257. DOI: https://doi.org/10.1109/SURV.2011.041110.00022
Copyright (c) 2024 FUDMA JOURNAL OF SCIENCES
This work is licensed under a Creative Commons Attribution 4.0 International License.
FUDMA Journal of Sciences
