Deep-Learning-Based Intrusion Detection for Autonomous Vehicle-Following Systems
Journal
IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC
Journal Volume
2021-September
Pages
865-872
Date Issued
2021
Author(s)
Abstract
Autonomous vehicle-following systems, including Adaptive Cruise Control (ACC) and Cooperative Adaptive Cruise Control (CACC), improve safety, efficiency, and string stability for a vehicle (the ego vehicle) following its leading vehicle. The ego vehicle senses or receives information, such as the position, velocity, acceleration, or even intention, of the leading vehicle and controls its own behavior. However, it has been shown that sensors and wireless channels are vulnerable to security attacks, and attackers can modify data sensed from sensors or received from other vehicles. To address this problem, in this paper, we design three types of stealthy attacks on ACC or CACC inputs, where the stealthy attacks can deceive a rule-based detection approach and impede system properties (collision-freeness and vehicle-following distance). We then develop two deep-learning models, a predictor-based model and an encoder-decoder- based model to detect the attacks, where the two models do not need attacker models for training. The experimental results demonstrate the respective strengths of different models and lead to a methodology for the design of learning-based intrusion detection approaches. © 2021 IEEE.
Other Subjects
Adaptive control systems; Autonomous vehicles; Chemical detection; Deep learning; Intrusion detection; Autonomous vehicle-following; Cooperative adaptive cruise control; Intrusion-Detection; Leading vehicle; Safety stability; Sensor channels; String stability; Vehicle following; Vehicle following systems; Wireless channel; Adaptive cruise control
Type
conference paper