Journal
PROCEEDINGS OF THE 2021 IEEE INTERNATIONAL CONFERENCE ON CYBER SECURITY AND RESILIENCE (IEEE CSR)
Volume -, Issue -, Pages 218-225Publisher
IEEE
DOI: 10.1109/CSR51186.2021.9527951
Keywords
Intelligent Transportation Systems; cybersecurity; Sybil attack; Traffic Signal Control; Reinforcement Learning
Funding
- (Natural Sciences & Engineering Research Council of Canada NSERC)
- Canada Research Chairs (CRC)
Ask authors/readers for more resources
Intelligent Transportation Systems utilize new technologies to provide intelligent road services and optimize decision-making, but they are vulnerable to cyber attacks, especially in dynamic TSC systems. This paper highlights the threat of intelligent attacks using DRL on TSC systems, which can lead to traffic congestion and other serious issues.
Intelligent Transportation Systems (ITS) combine emerging communication, computer, and system technologies to deliver intelligent road traffic services and optimize decision making within the transportation infrastructure. The advancement of connected vehicles, which generate dynamic data through wireless communications, enables ITS to improve their efficiency, especially in Traffic Signal Control (TSC), which is the backbone of traffic flow scheduling. However, wireless communications channels are vulnerable to various types of cyber-attacks and can pose serious threats to dynamic TSC systems. Attackers may attempt to manipulate normal traffic flows and cause severe traffic congestion. Deep Reinforcement Learning (DRL) is a powerful technique that has been used to improve TSC systems performance in real-time environments. However, it can be used by attackers to exploit the dynamics of the ITS and learn the optimal attack policy under the lack of deterministic information about system behavior. In this work, to highlight and exploit existing vulnerabilities in TSC systems, we leverage DRL to create an intelligent Sybil attack on a traffic intersection, wherein connected vehicles with fake identities are optimally placed to alter traffic signal timings by corrupting traffic data. The results show that this attack leads to substantial increase in the vehicles' travel time and yields disastrous traffic congestion, especially if carried out for a prolonged period of time, which will give rise to serious problems such as higher fuel consumption and air pollution in heavily dense cities. In the presence of such intelligent attacks, the design assumptions of existing TSC systems become highly questionable.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available