volume | PIER Journals

Abstract

The problem of Radio Frequency (RF) jamming is a significant threat to any current wireless communication network, since a low-power source may seriously diminish or disrupt legitimate conditions of a transmission. Traditional methods of detection based generally on a fixed threshold or packet-level cues cannot be effectively sustained in the presence of adaptive and reactive jamming behavior as observed in real-world deployments. This paper introduces a system of RF signal monitoring and threat detection integrating frequency-domain feature extraction, supervised machine learning, and statistical signal characterization. Both time-domain (such as Received Signal Strength Indicator (RSSI) and Signal to Interference plus Noise Ratio (SINR) metrics and spectral quantities calculated with the Fast Fourier Transform (FFT) are being used to capture both temporary and persistent interference patterns. The hybrid ensemble approach in the form of Random Forest and XGBoost classifiers is implemented to achieve a balance among robustness, interpretability, and classification performance of various jammer types. Empirical testing with actual RF data demonstrates that the suggested method has an initial detection rate of 98 percent, and its performance does not degrade in the low signal-to-noise ratio regime. These findings imply that the combination of lightweight spectral analysis and ensemble learning is a feasible and scalable solution to real-time RF threat detection in dynamic wireless systems.

1. Puñal, Oscar, Ismet Aktaş, Caj-Julian Schnelke, Gloria Abidin, Klaus Wehrle, and James Gross, "Machine learning-based jamming detection for IEEE 802.11: Design and experimental evaluation," Proceeding of IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks 2014, 1-10, Sydney, NSW, Australia, Jun. 2014.
doi:10.1109/WoWMoM.2014.6918964

2. Feng, Zhutian and Cunqing Hua, "Machine learning-based RF jamming detection in wireless networks," 2018 Third International Conference on Security of Smart Cities, Industrial Control System and Communications (SSIC), 1-6, Shanghai, China, Oct. 2018.
doi:10.1109/SSIC.2018.8556709

3. Upadhyaya, Bikalpa, Sumei Sun, and Biplab Sikdar, "Machine learning-based jamming detection in wireless IoT networks," 2019 IEEE VTS Asia Pacific Wireless Communications Symposium (APWCS), 1-5, Singapore, 2019.
doi:10.1109/VTS-APWCS.2019.8851633

4. Hussain, Ahmed, Nada Abughanam, Junaid Qadir, and Amr Mohamed, "Jamming detection in IoT wireless networks: An edge-AI based approach," Proceedings of the 12th International Conference on the Internet of Things, 57-64, Delft, Netherlands, 2022.
doi:10.1145/3567445.3567456

5. Testi, Enrico, Luca Arcangeloni, and Andrea Giorgetti, "Machine learning-based jamming detection and classification in wireless networks," Proceedings of the 2023 ACM Workshop on Wireless Security and Machine Learning, 39-44, Guildford, UK, Jun. 2023.
doi:10.1145/3586209.359139

6. Arjoune, Youness, Fatima Salahdine, Md. Shoriful Islam, Elias Ghribi, and Naima Kaabouch, "A novel jamming attacks detection approach based on machine learning for wireless communication," 2020 International Conference on Information Networking (ICOIN), 459-464, Barcelona, Spain, Jan. 2020.
doi:10.1109/ICOIN48656.2020.9016462

7. Lee, Sun-Jin, Yu-Rim Lee, So-Eun Jeon, and Il-Gu Lee, "Machine learning-based jamming attack classification and effective defense technique," Computers & Security, Vol. 128, 103169, 2023.
doi:10.1016/j.cose.2023.103169 Google Scholar

8. Kosmanos, Dimitrios, Dimitrios Karagiannis, Antonios Argyriou, Spyros Lalis, and Leandros Maglaras, "RF jamming classification using relative speed estimation in vehicular wireless networks," Security and Communication Networks, Vol. 2021, No. 1, 9959310, 2021.
doi:10.1155/2021/9959310 Google Scholar

9. Li, Yuchen, Jamming detection and classification via conventional machine learning and deep learning with applications to UAVS, Purdue University, IN, United States, 2021.

10. Erpek, Tugba, Yalin E. Sagduyu, and Yi Shi, "Deep learning for launching and mitigating wireless jamming attacks," IEEE Transactions on Cognitive Communications and Networking, Vol. 5, No. 1, 2-14, 2019.
doi:10.1109/tccn.2018.2884910 Google Scholar

11. Li, Yuchen, Jered Pawlak, Joshua Price, Khair Al Shamaileh, Quamar Niyaz, Sidike Paheding, and Vijay Devabhaktuni, "Jamming detection and classification in OFDM-based UAVs via feature- and spectrogram-tailored machine learning," IEEE Access, Vol. 10, 16859-16870, 2022.
doi:10.1109/access.2022.3150020 Google Scholar

12. Zhang, Zhengguang and Marwan Krunz, "Detection and classification of smart jamming in Wi-Fi networks using machine learning," MILCOM 2023 --- 2023 IEEE Military Communications Conference (MILCOM), 919-924, Boston, MA, USA, 2023.
doi:10.1109/MILCOM58377.2023.10356126

13. Zahra, Fatima Tu, Yavuz Selim Bostanci, and Mujdat Soyturk, "Real-time jamming detection in wireless IoT networks," IEEE Access, Vol. 11, 70425-70442, 2023.
doi:10.1109/access.2023.3293404 Google Scholar

14. Grover, Kanika, Alvin Lim, and Qing Yang, "Jamming and anti-jamming techniques in wireless networks: A survey," International Journal of Ad Hoc and Ubiquitous Computing, Vol. 17, No. 4, 197-215, 2014.
doi:10.1504/ijahuc.2014.066419 Google Scholar

15. Panitsas, Ioannis, Yagmur Yigit, Leandros Tassiulas, Leandros Maglaras, and Berk Canberk, "Jamshield: A machine learning detection system for over-the-air jamming attacks," ICC 2025 --- IEEE International Conference on Communications, 1067-1072, Montreal, QC, Canada, Jun. 2025.
doi:10.1109/ICC52391.2025.11161395

16. Kuili, Samhita, Mohammadreza Amini, and Burak Kantarci, "A two-stage CAE-based federated learning framework for efficient jamming detection in 5G networks," ICC 2025 --- IEEE International Conference on Communications, 3394-3399, Montreal, QC, Canada, 2025.
doi:10.1109/ICC52391.2025.11162114

17. Jacovic, Marko, Xaime Rivas Rey, Geoffrey Mainland, and K. Dandekar, "Mitigating RF jamming attacks at the physical layer with machine learning," IET Communications, Vol. 17, No. 1, 12-28, 2023.
doi:10.1049/cmu2.12461 Google Scholar

Dr. Chinmay Kumar

Dr. Gourav Kumar

Dr. Sehejdeep Singh

Dr. Vritant Sood

Dr. Naveen Jaglan