TY - GEN
T1 - Context-Aware Drone Detection
AU - Oligeri, Gabriele
AU - Sciancalepore, Savio
N1 - Publisher Copyright:
© 2022 ACM.
PY - 2022/5/30
Y1 - 2022/5/30
N2 - Current commercial and research solutions for drones' detection do not make any assumption on the scenario deployment, as well as the unique mobility pattern associated with the drone's trajectory. Indeed, drones' trajectory is different from the one of people moving at the ground level, being independent of roads layout and obstacles on their path: drones fly directly towards their target, minimizing the travel time and the possibility of being detected. Grounding on this intuition, we propose CADD, a solution enabling drone detection via context-related information. CADD leverages a sensing infrastructure to locate and track all the devices in the area to be protected, and it distinguishes the trajectory of a drone as an anomaly with respect to a ground-truth of allowed trajectories - -the ones generated by the devices at the ground level, belonging to vehicles and users within them. We evaluated the performance of CADD over a real dataset of moving vehicles (taxi) in both urban and rural scenarios, resulting in an overall accuracy of 0.91 and 0.84, for the rural and the urban scenario, respectively.
AB - Current commercial and research solutions for drones' detection do not make any assumption on the scenario deployment, as well as the unique mobility pattern associated with the drone's trajectory. Indeed, drones' trajectory is different from the one of people moving at the ground level, being independent of roads layout and obstacles on their path: drones fly directly towards their target, minimizing the travel time and the possibility of being detected. Grounding on this intuition, we propose CADD, a solution enabling drone detection via context-related information. CADD leverages a sensing infrastructure to locate and track all the devices in the area to be protected, and it distinguishes the trajectory of a drone as an anomaly with respect to a ground-truth of allowed trajectories - -the ones generated by the devices at the ground level, belonging to vehicles and users within them. We evaluated the performance of CADD over a real dataset of moving vehicles (taxi) in both urban and rural scenarios, resulting in an overall accuracy of 0.91 and 0.84, for the rural and the urban scenario, respectively.
KW - anomaly detection
KW - context-aware intrusion detection
KW - drone detection
KW - localization
KW - unmanned aerial vehicles
UR - https://www.scopus.com/pages/publications/85134403316
U2 - 10.1145/3494107.3522777
DO - 10.1145/3494107.3522777
M3 - Conference contribution
AN - SCOPUS:85134403316
T3 - CPSS 2022 - Proceedings of the 8th ACM Cyber-Physical System Security Workshop
SP - 63
EP - 71
BT - CPSS 2022 - Proceedings of the 8th ACM Cyber-Physical System Security Workshop
PB - Association for Computing Machinery, Inc
T2 - 8th ACM Cyber-Physical System Security Workshop, CPSS 2022, co-located with ACM AsiaCCS 2022
Y2 - 30 May 2022
ER -