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In recent years,unmanned air vehicles(UAVs)are widely used in many military and civilian applications.With the big amount of UAVs operation in air space,the potential security and privacy problems are arising.This can lead to consequent harm for critical infrastructure in the event of these UAVs being used for criminal or terrorist purposes.Therefore,it is crucial to promptly identify the suspicious behaviors from the surrounding UAVs for some important regions.In this paper,a novel fuzzy logic based UAV behavior detection system has been presented to detect the different levels of risky behaviors of the incoming UAVs.The heading velocity and region type are two input indicators proposed for the risk indicator output in the designed fuzzy logic based system.The simulation has shown the effective and feasible of the proposed algorithm in terms of recall and precision of the detection.Especially,the suspicious behavior detection algorithm can provide a recall of 0.89 and a precision of 0.95 for the high risk scenario in the simulation.
In recent years, unmanned air vehicles (UAVs) are widely used in many military and civilian applications. Due to this big amount of UAVs operation in air space, the potential security and privacy problems are arising. This can lead to consequent harm for critical infrastructure in the event of these UAVs being used for criminal or terrorist purposes.Therefore, it is crucial to promptly identify the suspicious behaviors from the surrounding UAVs for some important regions. In this paper, a novel fuzzy logic based UAV behavior detection system has been presented to detect the different levels of risky behaviors of the incoming UAVs.The heading velocity and region type are two input indicators proposed for the risk indicator output in the designed fuzzy logic based system. The simulation has shown the effective and feasible of the proposed algorithm in terms of recall and precision of the detection .Especially, the suspicious behavior detection algorithm can provide a recall of 0.89 and a precision of 0 .95 for the high risk scenario in the simulation.