Visible to the public Attack Detection and Countermeasures for Autonomous Navigation

TitleAttack Detection and Countermeasures for Autonomous Navigation
Publication TypeConference Paper
Year of Publication2021
AuthorsArafin, Md Tanvir, Kornegay, Kevin
Conference Name2021 55th Annual Conference on Information Sciences and Systems (CISS)
Keywordsautonomous navigation, autonomous robots, Autonomous vehicles, global positioning system (GPS), Hardware, human factors, mobile robotics, Navigation, pubcrawl, resilience, Resiliency, Robot sensing systems, Robot Trust, security, spoofing attacks, visual and inertial odometry, visualization
AbstractAdvances in artificial intelligence, machine learning, and robotics have profoundly impacted the field of autonomous navigation and driving. However, sensor spoofing attacks can compromise critical components and the control mechanisms of mobile robots. Therefore, understanding vulnerabilities in autonomous driving and developing countermeasures remains imperative for the safety of unmanned vehicles. Hence, we demonstrate cross-validation techniques for detecting spoofing attacks on the sensor data in autonomous driving in this work. First, we discuss how visual and inertial odometry (VIO) algorithms can provide a root-of-trust during navigation. Then, we develop examples for sensor data spoofing attacks using the open-source driving dataset. Next, we design an attack detection technique using VIO algorithms that cross-validates the navigation parameters using the IMU and the visual data. Following, we consider hardware-dependent attack survival mechanisms that support an autonomous system during an attack. Finally, we also provide an example of spoofing survival technique using on-board hardware oscillators. Our work demonstrates the applicability of classical mobile robotics algorithms and hardware security primitives in defending autonomous vehicles from targeted cyber attacks.
DOI10.1109/CISS50987.2021.9400224
Citation Keyarafin_attack_2021