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Jammer Detection based on Artificial Neural Networks: A Measurement Study

Published: 15 May 2019 Publication History

Abstract

Wireless networks are prone to jamming attacks due to the broadcast nature of the wireless transmission environment. The effect of jamming attacks can be further increased as the jammers can focus their signals on reference signals of the transmitters, to further deteriorate the transmission performance. In this paper, we aim to jointly determine the presence of the jammer, along with its attack characteristics by using neural networks. Two neural network architectures are implemented; deep convolutional neural networks and deep recurrent neural networks. The presence of jammer and the transmitter and the type of the jammer is determined through a diverse set of scenarios that are implemented on software defined radios using orthogonal frequency division multiplexing based signaling. To improve the detection performance, prepossessing techniques are applied. Test results show that the proposed approach can effectively detect and classify the jamming attacks with around 85% accuracy.

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  • (2024)XAI-based RF Jamming Detection Framework for Vehicular Networks in Battlefield Environment2024 5th International Conference for Emerging Technology (INCET)10.1109/INCET61516.2024.10593294(1-6)Online publication date: 24-May-2024
  • (2024)LSTM-Based Jamming Detection and Forecasting Model Using Transport and Application Layer Parameters in Wi-Fi Based IoT SystemsIEEE Access10.1109/ACCESS.2024.337167312(32944-32958)Online publication date: 2024
  • (2024)Investigating the effects of destructive factors on pulse repetition interval modulation type recognition using deep convolutional neural networks based on transfer learningIET Radar, Sonar & Navigation10.1049/rsn2.12660Online publication date: 6-Nov-2024
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    cover image ACM Conferences
    WiseML 2019: Proceedings of the ACM Workshop on Wireless Security and Machine Learning
    May 2019
    76 pages
    ISBN:9781450367691
    DOI:10.1145/3324921
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    Published: 15 May 2019

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    Author Tags

    1. Physical layer security
    2. jamming attacks
    3. neural networks

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    View all
    • (2024)XAI-based RF Jamming Detection Framework for Vehicular Networks in Battlefield Environment2024 5th International Conference for Emerging Technology (INCET)10.1109/INCET61516.2024.10593294(1-6)Online publication date: 24-May-2024
    • (2024)LSTM-Based Jamming Detection and Forecasting Model Using Transport and Application Layer Parameters in Wi-Fi Based IoT SystemsIEEE Access10.1109/ACCESS.2024.337167312(32944-32958)Online publication date: 2024
    • (2024)Investigating the effects of destructive factors on pulse repetition interval modulation type recognition using deep convolutional neural networks based on transfer learningIET Radar, Sonar & Navigation10.1049/rsn2.12660Online publication date: 6-Nov-2024
    • (2024)Artificial intelligence empowered physical layer security for 6G: State-of-the-art, challenges, and opportunitiesComputer Networks10.1016/j.comnet.2024.110255242(110255)Online publication date: Apr-2024
    • (2024)Machine Learning-Based Cooperative Clustering for Detecting and Mitigating Jamming Attacks in beyond 5G NetworksInformation Systems Frontiers10.1007/s10796-024-10534-6Online publication date: 6-Sep-2024
    • (2023)Comparative Analysis of Deep Learning Models for Detecting Jamming Attacks in Wi-Fi Network Data2023 12th IFIP/IEEE International Conference on Performance Evaluation and Modeling in Wired and Wireless Networks (PEMWN)10.23919/PEMWN58813.2023.10304936(1-6)Online publication date: 27-Sep-2023
    • (2023)eSWORD: Implementation of Wireless Jamming Attacks in a Real-World Emulated Network2023 IEEE Wireless Communications and Networking Conference (WCNC)10.1109/WCNC55385.2023.10118687(1-6)Online publication date: Mar-2023
    • (2023)Detection of Jamming Attacks via Source Separation and Causal InferenceIEEE Transactions on Communications10.1109/TCOMM.2023.328146771:8(4793-4806)Online publication date: Aug-2023
    • (2023)A Hybrid Jamming Detection Algorithm for Wireless Communications: Simultaneous Classification of Known Attacks and Detection of Unknown AttacksIEEE Communications Letters10.1109/LCOMM.2023.327569427:7(1769-1773)Online publication date: Jul-2023
    • (2023)Deep Learning-Enabled Deceptive Jammer Detection for Low Probability of Intercept CommunicationsIEEE Systems Journal10.1109/JSYST.2022.318048117:2(2166-2177)Online publication date: Jun-2023
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