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We propose PiNcH, a methodology to detect the presence of a drone, itscurrent status, and its movements by leveraging just the communication trafficexchanged between the drone and its Remote Controller (RC). PiNcH is builtapplying standard classification algorithms to the eavesdropped traffic,analyzing features such as packets inter-arrival time and size. PiNcH is fullypassive and it requires just cheap and general-purpose hardware. To evaluatethe effectiveness of our solution, we collected real communication tracesoriginated by a drone running the widespread ArduCopter open-source firmware,currently mounted on-board of a wide range (30+) of commercial amateur drones.We tested our solution against different publicly available wireless traces.The results prove that PiNcH can efficiently and effectively: (i) identify thepresence of the drone in several heterogeneous scenarios; (ii) identify thecurrent state of a powered-on drone, i.e., flying or lying on the ground; (iii)discriminate the movements of the drone; and, finally, (iv) enjoy a reducedupper bound on the time required to identify a drone with the requested levelof assurance. The effectiveness of PiNcH has been also evaluated in thepresence of both heavy packet loss and evasion attacks. In this latter case,the adversary modifies on purpose the profile of the traffic of the drone-RClink to avoid the detection. In both the cited cases, PiNcH continues enjoyinga remarkable performance. Further, the comparison against state of the artsolution confirms the superior performance of PiNcH in several scenarios. Notethat all the drone-controller generated data traces have been released asopen-source, to allow replicability and foster follow-up. Finally, the qualityand viability of our solution, do prove that network traffic analysis can besuccessfully adopted for drone identification and status discrimination.

PiNcH: an Effective, Efficient, and Robust Solution to Drone Detection via Network Traffic Analysis