Barak Davidovich (Ben-Gurion University of the Negev), Ben Nassi (Ben-Gurion University of the Negev) and Yuval Elovici (Ben-Gurion University of the Negev)

In this study, we propose an innovative method for the real-time detection of GPS spoofing attacks targeting drones, based on the video stream captured by a drone’s camera. The proposed method collects frames from the video stream and their location (GPS); by calculating the correlation between each frame, our method can detect a GPS spoofing on a drone. We first analyze the performance of the suggested method in a controlled environment by conducting experiments on a flight simulator that we developed. Then, we analyze its performance in the real world using a DJI drone. Our method can provide different levels of security against GPS spoofing attacks, depending on the detection interval required; for example, it can provide a high level of security to a drone flying at an altitude of 50-100 meters over an urban area at an average speed of 4 km/h in conditions of low ambient light; in this scenario, the proposed method can provide a level of security that detects any GPS spoofing attack in which the spoofed location is a distance of 1-4 meters (an average of 2.5 meters) from the real location.

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Jianfeng Li (The Hong Kong Polytechnic University), Shuohan Wu (The Hong Kong Polytechnic University), Hao Zhou (The Hong Kong Polytechnic University), Xiapu Luo (The Hong Kong Polytechnic University), Ting Wang (Penn State), Yangyang Liu (The Hong Kong Polytechnic University), Xiaobo Ma (Xi'an Jiaotong University)

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Simin Ghesmati (Uni Wien, SBA Research), Walid Fdhila (Uni Wien, SBA Research), Edgar Weippl (Uni Wien, SBA Research)

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Alireza Mohammadi (University of Michigan-Dearborn), Hafiz Malik (University of Michigan-Dearborn) and Masoud Abbaszadeh (GE Global Research)

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Pritam Dash (University of British Columbia) and Karthik Pattabiraman (University of British Columbia)

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