Gabriele Oligeri
ABSTRACT
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.
- S. Al-Emadi, A. Al-Ali, A. Mohammad, and A. Al-Ali. 2019. Audio Based Drone Detection and Identification using Deep Learning. In 15th International Wireless Communications Mobile Computing Conference (IWCMC) . 459--464. https://doi.org/10.1109/IWCMC.2019.8766732Google Scholar
- Aladdin Consortium. 2021. Aladdin -- Advanced hoListic Adverse Drone Detection, Identification & Neutralization . http://aladdin.eu . (Accessed: 2021--10--22.Google Scholar
- R. Altawy and A. Youssef. 2016. Security, privacy, and safety aspects of civilian drones: A survey . ACM Transactions on Cyber-Physical Systems , Vol. 1, 2 (2016), 1--25.Google ScholarDigital Library
- M. Z. Anwar, Z. Kaleem, and A. Jamalipour. 2019. Machine Learning Inspired Sound-Based Amateur Drone Detection for Public Safety Applications . IEEE Transactions on Vehicular Technology , Vol. 68, 3 (2019), 2526--2534. https://doi.org/10.1109/TVT.2019.2893615Google ScholarCross Ref
- B. Alzahrani, et al. 2020. UAV assistance paradigm: State-of-the-art in applications and challenges . Journ. of Netw. and Comput. Applicat. (2020), 102706.Google Scholar
- BBC. 2019. Gatwick Airport: Drone attack grounds flights . http://www.bbc.co.uk/news/uk-england-sussex-4662375 . (Accessed: 2021--10--22.Google Scholar
- S. Birnbach, R. Baker, and I. Martinovic. 2017. Wi-fly?: Detecting privacy invasion attacks by consumer drones . (2017).Google Scholar
- I. Bisio, C. Garibotto, F. Lavagetto, A. Sciarrone, and S. Zappatore. 2019. Blind Detection: Advanced Techniques for WiFi-Based Drone Surveillance . IEEE Transactions on Vehicular Technology , Vol. 68, 1 (2019), 938--946. https://doi.org/10.1109/TVT.2018.2884767Google ScholarCross Ref
- P. Boccadoro, D. Striccoli, and L. Grieco. 2021. An extensive survey on the Internet of Drones . Ad Hoc Networks , Vol. 122 (2021), 102600.Google ScholarDigital Library
- L. Caceres Najarro, I. Song, and K. Kim. 2021. Fundamental Limitations and State-of-the-art Solutions for Target Node Localization in WSNs . (2021).Google Scholar
- DeDrone. 2021. Worldwide Drone Incidents . Available Online: hhttps://www.dedrone.com/resources/incidents/all.Google Scholar
- R. Di Pietro, G. Oligeri, and P. Tedeschi. 2019. JAM-ME: Exploiting Jamming to Accomplish Drone Mission. In 2019 IEEE Conference on Communications and Network Security (CNS). 1--9. https://doi.org/10.1109/CNS.2019.8802717Google ScholarCross Ref
- FAA. 2021. UAS Remote Identification Overview . Available Online: https://www.faa.gov/uas/getting_started/remote_id/.Google Scholar
- A. Koubâa, A. Allouch, M. Alajlan, Y. Javed, A. Belghith, and M. Khalgui. 2019. Micro Air Vehicle Link (MAVlink) in a Nutshell: A Survey . IEEE Access , Vol. 7 (2019), 87658--87680.Google ScholarCross Ref
- H. Liu, Z. Wei , Y. Chen, J. Pan, L. Lin, and Y. Ren. 2017. Drone Detection Based on an Audio-Assisted Camera Array. In IEEE Third International Conference on Multimedia Big Data (BigMM). 402--406. https://doi.org/10.1109/BigMM.2017.57Google ScholarCross Ref
- M. Mozaffari, et al. 2019. A Tutorial on UAVs for Wireless Networks: Applications, Challenges, and Open Problems . IEEE Communications Surveys & Tutorials , Vol. 21, 3 (2019).Google ScholarCross Ref
- L. Moreira-Matias, J. Gama, M. Ferreira, J. Mendes-Moreira, and L. Damas. 2013. Predicting Taxi--Passenger Demand Using Streaming Data. IEEE Transactions on Intelligent Transportation Systems , Vol. 14, 3 (2013), 1393--1402. https://doi.org/10.1109/TITS.2013.2262376Google ScholarDigital Library
- B. Nassi, R. Ben-Netanel, A. Shamir, and Y. Elovici. 2018. Game of drones-detecting streamed POI from encrypted FPV channel . arXiv preprint arXiv:1801.03074 (2018).Google Scholar
- B. Nassi, R. Ben-Netanel, A. Shamir, and Y. Elovici. 2019. Drones' Cryptanalysis - Smashing Cryptography with a Flicker. In 2019 IEEE Symposium on Security and Privacy (SP). 1397--1414. https://doi.org/10.1109/SP.2019.00051Google ScholarCross Ref
- P. Nguyen, H. Truong, M. Ravindranathan, A. Nguyen, R. Han, and T. Vu. 2017. Matthan: Drone Presence Detection by Identifying Physical Signatures in the Drone's RF Communication. In Proc. of the Annual Int. Conf. on Mobile Systems, Applications, and Services (MobiSys '17). 211--224.Google Scholar
- G. Oligeri, S. Sciancalepore, and R. Di Pietro. 2020. GNSS Spoofing Detection via Opportunistic IRIDIUM Signals (WiSec '20). Association for Computing Machinery, New York, NY, USA, 42--52. https://doi.org/10.1145/3395351.3399350Google ScholarDigital Library
- G. Oligeri, S. Sciancalepore, O. Ibrahim, and R. Di Pietro. 2019. Drive Me Not: GPS Spoofing Detection via Cellular Network: (Architectures, Models, and Experiments) (WiSec '19). Association for Computing Machinery, New York, NY, USA, 12--22. https://doi.org/10.1145/3317549.3319719Google ScholarDigital Library
- A. Otto, N. Agatz, J. Campbell, B. Golden, and E. Pesch. 2018. Optimization approaches for civil applications of unmanned aerial vehicles (UAVs) or aerial drones: A survey . Networks , Vol. 72, 4 (2018), 411--458.Google ScholarCross Ref
- S. Raponi, S. Sciancalepore, G. Oligeri, and R. Di Pietro. 2021. Road Traffic Poisoning of Navigation Apps: Threats and Countermeasures. IEEE Security Privacy (2021), 2--11. https://doi.org/10.1109/MSEC.2021.3110307Google Scholar
- T. Rappaport. 2001. Wireless Communications: Principles and Practice 2nd ed.). Prentice Hall PTR, USA.Google ScholarDigital Library
- Reuters. 2021. Saudi-led coalition says it intercepts armed drone fired at Abha airport . https://www.reuters.com/world/middle-east/saudi-led-coalition-says-intercepts-armed-drone-fired-abha-airport-2021-05--10/. (Accessed: 2021--10--22.Google Scholar
- SafeShore Consortium. 2021. SafeShore -- Increasing Border Security . http://safeshore.eu . (Accessed: 2021--10--22.Google Scholar
- S. Sciancalepore, O. Ibrahim, G. Oligeri, and R. Di Pietro. 2019. Detecting drones status via encrypted traffic analysis. In Proceedings of the ACM Workshop on Wireless Security and Machine Learning. 67--72.Google Scholar
- S. Sciancalepore, O. Ibrahim, G. Oligeri, and R. Di Pietro. 2020. PiNcH: An effective, efficient, and robust solution to drone detection via network traffic analysis . Computer Networks , Vol. 168 (2020), 107044.Google ScholarDigital Library
- J. Seybold. 2005. Introduction to RF propagation. John Wiley & Sons.Google Scholar
- D. Shin, D. Jung , D. Kim, J. Ham, and S. Park. 2017. A Distributed FMCW Radar System Based on Fiber-Optic Links for Small Drone Detection . IEEE Transactions on Instrumentation and Measurement , Vol. 66, 2 (2017), 340--347. https://doi.org/10.1109/TIM.2016.2626038Google ScholarCross Ref
- A. Shoufan, H. M. Al-Angari, M. F. A. Sheikh, and E. Damiani. 2018. Drone Pilot Identification by Classifying Radio-Control Signals . IEEE Transactions on Information Forensics and Security , Vol. 13, 10 (Oct 2018), 2439--2447.Google ScholarCross Ref
- Statista. 2021. Estimated size of the global commercial drone market in 2021 with a forecast for 2026 . https://www.statista.com/statistics/878018/global-commercial-drone-market-size/. Accessed: 2021--10--22.Google Scholar
- H. Sun, B. Oh , X. Guo, and Z. Lin. 2019. Improving the Doppler Resolution of Ground-Based Surveillance Radar for Drone Detection . IEEE Trans. Aerospace Electron. Systems , Vol. 55, 6 (2019), 3667--3673. https://doi.org/10.1109/TAES.2019.2895585Google ScholarCross Ref
- P. Tedeschi, G. Oligeri, and R. Di Pietro. 2020. Leveraging Jamming to Help Drones Complete Their Mission. IEEE Access , Vol. 8 (2020), 5049--5064. https://doi.org/10.1109/ACCESS.2019.2963105Google ScholarCross Ref
- P. Tedeschi, S. Sciancalepore, and R. Di Pietro. 2021. ARID: Anonymous Remote IDentification of Unmanned Aerial Vehicles. In Annual Computer Security Applications Conference. 207--218.Google Scholar
- T. Wall. 2016. Ordinary emergency: Drones, police, and geographies of legal terror . Antipode , Vol. 48, 4 (2016), 1122--1139.Google ScholarCross Ref
Index Terms
- Context-Aware Drone Detection
Recommendations
Radio Frequency-based Techniques of Drone Detection and Classification using Machine Learning
ICRAI '20: Proceedings of the 6th International Conference on Robotics and Artificial IntelligenceThis research paper provides a comprehensive survey review on drone detection using Radio Frequency (RF)-based techniques along with machine learning and localization algorithms. RF signals proved its effectiveness in detecting drones, however, due to ...
Automatic Control for Aerobatic Maneuvering of Agile Fixed-Wing UAVs
The use of unmanned aerial vehicles (UAVs) has become ubiquitous in a broadening range of applications, including many civilian uses. UAVs are typically categorized into two categories: conventional fixed-wing aircraft, which are associated with ...
ALFA: A dataset for UAV fault and anomaly detection
We present a dataset of several fault types in control surfaces of a fixed-wing unmanned aerial vehicle (UAV) for use in fault detection and isolation (FDI) and anomaly detection (AD) research. Currently, the dataset includes processed data for 47 ...
Comments