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 

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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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