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Game of Drones - Detecting Spying Drones Using Time Domain Analysis

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Cyber Security Cryptography and Machine Learning (CSCML 2021)

Part of the book series: Lecture Notes in Computer Science ((LNSC,volume 12716))


Drones have created a new threat to people’s privacy. We are now in an era in which anyone with a drone equipped with a video camera can use it to invade a subject privacy by filming the subject in his/her private space using encrypted First Person View (FPV) channel. Although many methods have been suggested to detect a nearby drone, they all suffer from the same shortcoming: they cannot detect what specifically is being captured and therefore they fail to distinguish between the legitimate use of a drone that does not invade a subject’s privacy (for example, neighbor’s drone flying and shoot his garden) and illegitimate use (same drone shooting the subject’s property), where in many cases depends on the orientation of the drone’s video camera rather than on the drone’s location. In this paper we present a method that utilizes a flicker in order to detect whether the drone’s camera is directed towards the private space by analyzing the encrypted video stream sent from the drone in real time. We investigate the influence of changing pixels on the transmitted traffic (in a lab setup). We leverage our conclusions and demonstrate how an interceptor can apply a side-channel attack to detect that a subject is video streamed by DJI Mavic drone from its encrypted FPV channel when the subject is located inside a private house.

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Nassi, B., Ben-Netanel, R., Shamir, A., Elovici, Y. (2021). Game of Drones - Detecting Spying Drones Using Time Domain Analysis. In: Dolev, S., Margalit, O., Pinkas, B., Schwarzmann, A. (eds) Cyber Security Cryptography and Machine Learning. CSCML 2021. Lecture Notes in Computer Science(), vol 12716. Springer, Cham.

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