Abstract
Small commercial Unmanned Aerial Systems (UASs), called drones in common language, pose significant security risks due to their agility, high availability and low price. There is, therefor, a growing need to develop methods for detection, localization and mitigation of malicious and other harmful operation of these drones. This paper presents our work towards autonomously localizing drone operators based only on following their path in the sky. We use a realistic simulation environment and collect the path of the drone when flown from different points of view. A deep neural network was trained to be able to predict the location of drone operators, given the path of the drones. The model is able to achieve prediction of the location of the location of the operator with 73% accuracy.
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Drozdowicz, J., et al.: 35 GHz FMCW drone detection system. In: IRS 2016
Ezzini, S., Berrada, I., Ghogho, M.: Who is behind the wheel? Driver identification and fingerprinting. J. Big Data 5, 9 (2018)
García, M., Viguria, A., Heredia, G., Ollero, A.: Minimal-time trajectories for interception of malicious drones in constrained environments. In: Tzovaras, D., Giakoumis, D., Vincze, M., Argyros, A. (eds.) ICVS 2019. LNCS, vol. 11754, pp. 734–743. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-34995-0_67
Russo, J., Woods, D., Shaffer, J.S., Jackson, B.A.: Countering threats to correctional institution security: identifying innovation needs to address current and emerging concerns. RAND Corp. (2019)
Shah, S., Dey, D., Lovett, C., Kapoor, A.: AirSim: high-fidelity visual and physical simulation for autonomous vehicles. In: Hutter, M., Siegwart, R. (eds.) Field and Service Robotics. SPAR, vol. 5, pp. 621–635. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-67361-5_40
Solomitckii, D., Gapeyenko, M., Semkin, V., Andreev, S., Koucheryavy, Y.: Technologies for efficient amateur drone detection in 5g millimeter-wave cellular infrastructure. IEEE Commun. Mag. 56(1), 43–50 (2018)
Wang, C.-C., Tomizuka, M.: Iterative learning control of mechanical systems with slow sampling rate position measurements. In: ASME 2008
Zhang, J., et al.: Attention-based convolutional and recurrent neural networks for driving behavior recognition using smartphone sensor data. IEEE Access 7, 148031–148046 (2019)
Zhang, J., et al.: A deep learning framework for driving behavior identification on in-vehicle CAN-BUS sensor data. Sensors 19(6), 1356 (2019)
Zhen, J.: Localization of unmanned aerial vehicle operators based on reconnaissance plane with multiple array sensors. IEEE Access 7, 105354–105362 (2019)
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Mashhadi, E., Oren, Y., Weiss, G. (2020). Can the Operator of a Drone Be Located by Following the Drone’s Path?. In: Dolev, S., Kolesnikov, V., Lodha, S., Weiss, G. (eds) Cyber Security Cryptography and Machine Learning. CSCML 2020. Lecture Notes in Computer Science(), vol 12161. Springer, Cham. https://doi.org/10.1007/978-3-030-49785-9_6
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DOI: https://doi.org/10.1007/978-3-030-49785-9_6
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