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Real-time attack detection on robot cameras: A self-driving car application

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Abstract

The Robot Operating System (ROS) are being deployed for multiple life critical activities such as self-driving cars, drones, and industries. However, the security has been persistently neglected, especially the image flows incoming from camera robots. In this paper, we perform a structured security assessment of robot cameras using ROS. We points out a relevant number of security flaws that can be used to take over the flows incoming from the robot cameras. Furthermore, we propose an intrusion detection system to detect abnormal flows. Our defense approach is based on images comparisons and unsupervised anomaly detection method. We experiment our approach on robot cameras embedded on a self-driving car.
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Dates and versions

hal-02063304 , version 1 (11-03-2019)

Identifiers

  • HAL Id : hal-02063304 , version 1

Cite

Sofiane Lagraa, Maxime Cailac, Sean Rivera, Frédéric Beck, Radu State. Real-time attack detection on robot cameras: A self-driving car application. IEEE IRC 2019 - Third IEEE International Conference on Robotic Computing, Feb 2019, Naples, Italy. ⟨hal-02063304⟩
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