Unmanned aerial vehicles (UAVs) have recently gained popularity due to their extensive uses in parcel delivery, wildlife conservation, agriculture, and the military. However, security worries with UAVs are developing, as UAV nodes are becoming appealing targets for assaults due to rapidly growing volumes and inadequate inbuilt security. This paper proposes an intelligent security system for UAVs that harness machine learning to detect cybersecurity attacks. It determines whether the signals coming to the UAV are benign, or offensive using a UAV attacks dataset containing two types of attacks: GPS spoofing and Jamming. In order to improve security in UAV networks, this research shows how machine learning methods may be utilized to categorize benign and malicious signals. Finally, the accuracy rate, recall, F1-score, precision, and confusion matrix of the tested ML algorithms are compared for efficacy. Compared to all other ML classifiers, the decision tree model performed well, with a maximum accuracy rate of 99.86% in detecting various attacks.


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    Titel :

    Intelligent Detection System for Spoofing and Jamming Attacks in UAVs


    Weitere Titelangaben:

    eng. Cyber-Physical Systems & crit. Infrastructures


    Beteiligte:
    Abd El-Latif, Ahmed A. (Herausgeber:in) / Maleh, Yassine (Herausgeber:in) / Mazurczyk, Wojciech (Herausgeber:in) / ELAffendi, Mohammed (Herausgeber:in) / I. Alkanhal, Mohamed (Herausgeber:in) / Jasim, Khadeeja Sabah (Autor:in) / Ali Alheeti, Khattab M. (Autor:in) / Najem Alaloosy, Abdul Kareem A. (Autor:in)

    Kongress:

    International Conference on Cybersecurity, Cybercrimes, and Smart Emerging Technologies ; 2022 ; Riyadh, Saudi Arabia May 10, 2022 - May 11, 2022



    Erscheinungsdatum :

    12.03.2023


    Format / Umfang :

    14 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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