Drones' Cryptanalysis - Smashing Cryptography with a Flicker
2019
In an "open skies" era in which drones fly among us, anew question arises: how can we tell whether a passing droneis being used by its operator for a legitimate purpose (e.g.,delivering pizza) or an illegitimate purpose (e.g., taking a peekat a person showering in his/her own house)? Over the years,many methods have been suggested to detect the presenceof a drone in a specific location, however since populatedareas are no longer off limits for drone flights, the previouslysuggested methods for detecting a privacy invasion attackare irrelevant. In this paper, we present a new method thatcan detect whether a specific POI (point of interest) is beingvideo streamed by a drone. We show that applying a periodicphysical stimulus on a target/victim being video streamed by adrone causes a watermark to be added to the encrypted videotraffic that is sent from the drone to its operator and howthis watermark can be detected using interception. Based onthis method, we present an algorithm for detecting a privacyinvasion attack. We analyze the performance of our algorithmusing four commercial drones (DJI Mavic Air, Parrot Bebop 2,DJI Spark, and DJI Mavic Pro). We show how our method canbe used to (1) determine whether a detected FPV (first-personview) channel is being used to video stream a POI by a drone,and (2) locate a spying drone in space; we also demonstratehow the physical stimulus can be applied covertly. In addition,we present a classification algorithm that differentiates FPVtransmissions from other suspicious radio transmissions. Weimplement this algorithm in a new invasion attack detectionsystem which we evaluate in two use cases (when the victimis inside his/her house and when the victim is being trackedby a drone while driving his/her car); our evaluation showsthat a privacy invasion attack can be detected by our systemin about 2-3 seconds.
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