Visible to the public Real-Time Attack Detection on Robot Cameras: A Self-Driving Car Application

TitleReal-Time Attack Detection on Robot Cameras: A Self-Driving Car Application
Publication TypeConference Paper
Year of Publication2019
AuthorsLagraa, S., Cailac, M., Rivera, S., Beck, F., State, R.
Conference Name2019 Third IEEE International Conference on Robotic Computing (IRC)
Date PublishedFeb. 2019
PublisherIEEE
ISBN Number978-1-5386-9245-5
Keywordsattack detection, automotive electronics, Cameras, car application, Human Behavior, human factors, image matching, images comparisons, images processing, intrusion detection system, mobile robots, object detection, Operating systems, Perturbation methods, policy-based governance, pubcrawl, real-time attack detection, resilience, Resiliency, Robot, robot cameras, Robot Operating System, robot operating systems, Robot vision systems, ROS, security, security assessment, security of data, self-driving cars, Service robots, telecommunication security, unsupervised anomaly detection method
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.

URLhttps://ieeexplore.ieee.org/document/8675588
DOI10.1109/IRC.2019.00023
Citation Keylagraa_real-time_2019