Multi-Agent Reinforcement Learning for Assessing False-Data Injection Attacks on Transportation Networks (2312.14625v2)

Abstract: The increasing reliance of drivers on navigation applications has made transportation networks more susceptible to data-manipulation attacks by malicious actors. Adversaries may exploit vulnerabilities in the data collection or processing of navigation services to inject false information, and to thus interfere with the drivers' route selection. Such attacks can significantly increase traffic congestions, resulting in substantial waste of time and resources, and may even disrupt essential services that rely on road networks. To assess the threat posed by such attacks, we introduce a computational framework to find worst-case data-injection attacks against transportation networks. First, we devise an adversarial model with a threat actor who can manipulate drivers by increasing the travel times that they perceive on certain roads. Then, we employ hierarchical multi-agent reinforcement learning to find an approximate optimal adversarial strategy for data manipulation. We demonstrate the applicability of our approach through simulating attacks on the Sioux Falls, ND network topology.

• The paper introduces a hierarchical multi-agent reinforcement learning framework to assess worst-case false-data injection attacks on traffic networks.
• It details the use of local and global agents to simulate coordinated attacks on the Sioux Falls network, outperforming traditional methods by 10-50%.
• The findings suggest that robust multi-agent RL strategies can significantly enhance the detection and mitigation of cyber threats in transportation systems.

Understanding False-Data Injection in Traffic Networks

Overview of Threats to Navigation Systems

Modern drivers often rely on navigation applications to guide their travel, making transportation systems more vulnerable to data manipulation attacks. These attacks can have severe consequences, as navigation apps are critical for accessing services and ensuring the smooth flow of logistics. Malicious actors can manipulate the data processed by navigation services, leading to increased travel times, traffic congestion, and disruptions to essential services. The researchers have crafted a computational framework to assess the worst-case scenarios of such false-data injection attacks on transportation networks.

The Hierarchical Approach to Identifying Vulnerabilities

To model the adversarial threats effectively, the researchers used a hierarchical multi-agent reinforcement learning (HMARL) framework. They categorized the adversarial threat actor as an entity capable of distorting drivers' perceptions of travel times on certain roads. They incorporated a hierarchical system with two levels of agents: local agents that strategize to distribute false information locally while adhering to a set attack budget, and a global agent that coordinates these local efforts. This system was tested against a Sioux Falls, ND network topology to measure its effectiveness.

Insights from Simulation Results

The simulation studies, particularly the Sioux Falls network trial, showed the HMARL approach could successfully approximate optimal strategies for false information injection. Notably, the framework outperformed traditional reinforcement learning methods and heuristic approaches by 10-50%, depending on the allocated attack budget. This indicates that the hierarchical multi-agent approach is a viable solution for better understanding the potential vulnerabilities inherent in navigation applications regarding traffic information reliability.

Future Directions for Traffic Network Security

This research highlights the importance of developing strategies to counter false-data attacks on traffic systems. Future work could focus on identifying these data manipulation attacks with greater precision. Considering the positive outcomes achieved, pursuing deep reinforcement learning and competitive multi-agent algorithms could deliver even more robust defense mechanisms. With the continuous enhancement of machine-learning techniques, particularly within multi-agent systems, researchers can better prepare and protect our transportation networks from potential cyber threats.