INVESTIGATING A TRANSPARENT AND INTERPRETABLE DEEP LEARNING MODEL FOR ENHANCED JAMMING ATTACK DETECTION IN UAV COMMUNICATION NETWORKS

Authors

  • Oluwadare Michael Ayegun

    Federal University of Technology Akure, Ondo State, Nigeria

  • Akingbade Kayode Francis

    Federal University of Technology Akure, Ondo State, Nigeria

  • Popoola Julius Jide

    Federal University of Technology Akure, Ondo State, Nigeria

  • Ubochi Brendan Chijioke

    Federal University of Technology Akure, Ondo State, Nigeria

DOI:
https://doi.org/10.51459/futajeet.2025.19.1.482
Keywords:
Unmanned Aerial Vehicle, Wireless Communication Network, Radio Frequency, Jamming Attack, Jamming Attack Detection, Convolutional Neural Network; Ablation Study
Abstract

Unmanned Aerial Vehicles (UAVs) have become indispensable in military and civil applications, playing a crucial role in intelligence gathering for tactical military operations and logistics delivery services. The operational effectiveness of UAVs anchors on secure and reliable radio frequency communication links, which can be susceptible to Jamming Attack (JA) threats. Among the current techniques used for JA detection (JAD) in wireless communication networks, Convolution Neural Network (CNN) models have demonstrated improved detection performance across individual and multiple jammer types. However, there is insufficient information on the model’s transparency and the relative contributions of the components of the CNN model in JAD. In this work, an ablation study is performed on the developed CNN model in order to examine the contributions or impact of key components on the overall detection accuracy of the model. By systematically removing each of these components and then training the modified CNN model on the same dataset under identical hyperparameter conditions with the baseline model, the JAD performance accuracy was evaluated. The results reveal that the convolutional layers have the biggest impact on the overall performance of the JAD model, degrading the detection accuracy from 97.0% to 65.3% when ablated. The study highlights the importance of key components of the CNN model, for achieving effective JAD in UAVs and similar sensitive wireless communication networks, thus ensuring secure operations.  

 

Author Biographies
  1. Oluwadare Michael Ayegun, Federal University of Technology Akure, Ondo State, Nigeria

    Department of Electrical and Electronics Engineering, 

  2. Akingbade Kayode Francis, Federal University of Technology Akure, Ondo State, Nigeria

    Department of Electrical and Electronics Engineering, School of Electrical Systems Engineering 

  3. Popoola Julius Jide , Federal University of Technology Akure, Ondo State, Nigeria

    Department of Electrical and Electronics Engineering, School of Electrical Systems Engineering 

  4. Ubochi Brendan Chijioke , Federal University of Technology Akure, Ondo State, Nigeria

    Department of Electrical and Electronics Engineering, School of Electrical Systems Engineering 

References

Abate A. F., Cimmino L., and Lorenzo-Navarro J. (2023). An ablation study on part-based face analysis using a Multi-input Convolutional Neural Network and Semantic Segmentation. Pattern Recognition Letters, Vol. 173, pp. 45 - 49.

Abiodun T. F. (2020). Usage of Drones or Unmanned Aerial Vehicles (UAVs) For Effective Aerial Surveillance, Mapping System and Intelligence Gathering in Combating Insecurity in Nigeria. African Journal of Social Sciences and Humanities Research, Vol. 3(2), pp. 29 - 44.

Alrefaei F. (2024). Machine Learning for Intrusion Detection into Unmanned Aerial System 6G Networks. Doctoral Dissertations and Master's Theses. 815.

Alvear O., Zema N. R., Natalizio E., and Calafate C. T. (2017). Using UAV-Based Systems to Monitor Air Pollution in Areas with Poor Accessibility. Journal of advanced Transportation, Vol. 2017(1), pp. 1 - 14.

Arjoune Y., Salahdine F., Islam M. S., Ghribi E., and Kaabouch N. (2020). A novel jamming attacks detection approach based on machine learning for wireless communication. International Conference on Information Networking, pp. 459 - 464.

Arthur M. P. (2019). Detecting signal spoofing and jamming attacks in UAV networks using a lightweight IDS. 2019 International Conference on Computer, Information and Telecommunication Systems (CITS), pp. 1 - 5.

Assaduzzaman M., Dutta M., Saha A., and Paul S. G. (2024). ALSA-3: Customized CNN model through ablation study for Alzheimer’s disease classification. Informatics in Medicine Unlocked, Vol. 50, pp. 1 - 17.

Duan B., Yin D., Cong Y., Zhou H., Xiang X., and Shen L. (2018). Antijamming path planning for unmanned aerial vehicles with imperfect jammer information. IEEE International Conference on Robotics and Biomimetics (ROBIO), pp. 729 - 735.

Fei Q., Xuetian Z., Ge M., Michel K. and Wei L. (2019). UAV Network and IoT in the Sky for Future Smart Cities. IEEE Network, Vol. 33(2), pp. 96 - 101. DOI: 10.1109/ MNET.2019.1800250.

Gao X., Jia H., Chen Z., Yuan G., and Yang S. (2020). UAV security situation awareness method based on semantic analysis. IEEE International Conference on Power, Intelligent Computing and Systems (ICPICS), pp. 272 - 276.

Gecgel S., Goztepe C., and Kurt G. K. (2019). Jammer detection based on artificial neural networks: A measurement study. Proceedings of the ACM Workshop on Wireless Security and Machine Learning, pp. 43 - 48.

Greco C., Pace P., Basagni S., and Fortino G. (2021). Jamming detection at the edge of drone networks using multi-layer perceptions and decision trees. Applied Soft Computing, Vol. 111, pp. 107806.

Hafeez S., Khan A. R., and Al-Quran M. M. (2023). Blockchain-Assisted UAV Communication Systems: A Comprehensive Survey. In IEEE Open Journal of Vehicular Technology, Vol. 4, pp. 558 - 580.

Kaleem Z., Yousaf M., Qamar A., Ahmad A., Duong T. Q., Choi W., and Jamalipour A. (2019). UAV-empowered disaster-resilient edge architecture for delay-sensitive communication. IEEE Network, Vol. 33(6), pp. 124 - 132.

Laghari, A. A., Jumani, A. K., Laghari, R. A., and Nawaz, H. (2023). Unmanned aerial vehicles: A review. Cognitive Robotics, Vol. 3, pp. 8 - 22.

Manesh M. R. and Kaabouch N. (2019). Cyber-attacks on unmanned aerial system networks: Detection, countermeasure, and future research directions. Computers & Security, Vol. 85, pp. 386 - 401.

Mowla N. I., Tran N. H., Doh I., and Chae K. (2019). Federated learning-based cognitive detection of jamming attack in flying ad-hoc network. IEEE Access, Vol. 8, pp. 4338 - 4350.

Naqvi S. A. R., Hassan S. A., Pervaiz H., and Ni Q. (2018). Drone-Aided Communication as a Key Enabler for 5G and Resilient Public Safety Networks. IEEE Communications Magazine Vol. 56(1), pp. 36 - 42. DOI: 10.1109/ MCOM.2017.1700451.

Paredes J., Jacinto C., Ramírez R., Vargas I., and Trujillano L. (2016). Simplified fuzzy-pd controller for behavior mixing and improved performance in quadcopter attitude control systems. In 2016 IEEE ANDESCON, pp. 1 - 4.

Rydberg A., Söderström M., Hagner O., Börjesson T. (2007). Field specific overview of crops using UAV (Unmanned Aerial Vehicle). Precision Agriculture’07, pp. 357-364.

Sedjelmaci H., Senouci S. M., and Ansari N. (2017). A hierarchical detection and response system to enhance security against lethal cyber-attacks in UAV networks. IEEE Transactions on Systems, Man, and Cybernetics: Systems, Vol. 48(9), pp. 1594 - 1606.

Slimane H. O., Benouadah S., Khoei T. T., and Kaabouch N. (2022). A Light Boosting-based ML Model for Detecting Deceptive Jamming Attacks on UAVs. 2022 IEEE 12th Annual Computing and Communication Workshop and Conference (CCWC), pp. 328 - 333.

Ukwueze, T. K., Nwafor, S. C., Ugwueze, K. O., Nnadozie, E. C., Odo, M., Ezechi, U., Chukwuma, C. K., Okafor, K., and Ani, A. O. (2024). Design and Implementation of an Unmanned Aerial Vehicle (UAV) for Image Capture in Enterprise Farming. Nigerian Journal of Technology, Vol. 43(1), pp. 150 - 158; https://doi.org/10.4314/njt.v43i1.17

Wu S., Li Y., Wang Z., Tan Z., and Pan Q. (2023). A highly interpretable framework for generic low-cost UAV attack detection. IEEE Sensors Journal, Vol. 23(7), pp. 7288 -7300.

Yaacoub J. P., Noura H., Salman O., and Chehab A. (2020). Security analysis of drone’s systems: Attacks, limitations, and recommendations. Internet of Things, Vol. 11, pp. 100218.

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2025-05-30
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Vol. 19 No. 1 (2025): FUTA Journal of Engineering and Engineering Technology
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How to Cite

INVESTIGATING A TRANSPARENT AND INTERPRETABLE DEEP LEARNING MODEL FOR ENHANCED JAMMING ATTACK DETECTION IN UAV COMMUNICATION NETWORKS. (2025). FUTA JOURNAL OF ENGINEERING AND ENGINEERING TECHNOLOGY, 19(1), 215-225. https://doi.org/10.51459/futajeet.2025.19.1.482

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