Utilizing Deep Learning Models to Detect Jamming and Spoofing Attacks on Unmanned Aerial Vehicles
Open Access DepositedThis area of study aims to enhance detection of malicious attacks on Unmanned Aerial Vehicle (UAV) sensors. As the use of UAVs—aerial devices that operate without a human pilot onboard—expands in various fields, the threats posed by jamming and spoofing attacks have become increasingly significant. This research explores the application of Deep Learning (DL) techniques, specifically Multilayer Perceptron (MLP) and Radial Basis Function Network (RBFN) models, to enhance the detection of these malicious activities targeting UAVs. By leveraging a comprehensive dataset simulating different jamming and spoofing scenarios, the researcher developed and trained these models to effectively identify and classify attacks with high accuracy, precision, recall and F-measure scores. The results highlight the potential of DL algorithms as key tools in proactively detecting malicious UAV operational activities, in turn ensuring precise and timely mitigation of these threats, including placing proactive measures in place to limit attacks.
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