A Multi-Agent Security Testbed for the Analysis of Attacks and Defenses in Collaborative Sensor Fusion (2401.09387v1)

Abstract: The performance and safety of autonomous vehicles (AVs) deteriorates under adverse environments and adversarial actors. The investment in multi-sensor, multi-agent (MSMA) AVs is meant to promote improved efficiency of travel and mitigate safety risks. Unfortunately, minimal investment has been made to develop security-aware MSMA sensor fusion pipelines leaving them vulnerable to adversaries. To advance security analysis of AVs, we develop the Multi-Agent Security Testbed, MAST, in the Robot Operating System (ROS2). Our framework is scalable for general AV scenarios and is integrated with recent multi-agent datasets. We construct the first bridge between AVstack and ROS and develop automated AV pipeline builds to enable rapid AV prototyping. We tackle the challenge of deploying variable numbers of agent/adversary nodes at launch-time with dynamic topic remapping. Using this testbed, we motivate the need for security-aware AV architectures by exposing the vulnerability of centralized multi-agent fusion pipelines to (un)coordinated adversary models in case studies and Monte Carlo analysis.

• The paper introduces MAST, a testbed enabling realistic simulation of both coordinated and uncoordinated adversarial attacks in sensor fusion pipelines.
• It leverages the ROS framework to integrate advanced vehicular datasets and offers flexible, dynamic scenario setups through topic remapping.
• Monte Carlo evaluations reveal significant vulnerabilities in centralized multi-agent fusion pipelines, highlighting the need for robust integrity checks.

A Multi-Agent Security Testbed for Analyzing Security of Collaborative Sensor Fusion

This article presents the development and evaluation of the Multi-Agent Security Testbed (MAST), a framework designed for assessing security threats and defenses in multi-sensor, multi-agent (MSMA) collaborative sensor fusion pipelines. Situated within the burgeoning field of autonomous vehicles (AVs), this work addresses the vulnerability of MSMA architectures to adversarial attacks by establishing a platform that supports a comprehensive range of security analyses.

Overview

The motivation behind this work stems from the increasing incorporation of multi-agent systems in AV technologies, which aim to enhance safety and operational effectiveness through collaborative autonomy enabled by sensor fusion and vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication. Despite significant investment in these technologies, their corresponding security protocols remain underdeveloped, thus exposing them to potential cyber threats.

MAST, constructed within the Robot Operating System (ROS) framework, serves as a versatile testbed that integrates advanced vehicular datasets. It establishes a bridge between AV development tools and real-time simulation environments, thereby facilitating the deployment of variable agent configurations and adversary models. The framework also features dynamic topic remapping for flexible scenario setup, which is critical for deploying complex multi-agent scenarios.

Methodology

The testbed's primary innovation lies in its capability to simulate both coordinated and uncoordinated adversarial attacks with variable numbers of adversary nodes. The coordinated attacks consider adversarial nodes that can communicate with one another to launch synchronized disruptions in MSMA systems, while uncoordinated attacks model scenarios where each adversarial node operates independently.

The authors implemented adversarial attacks at various levels of the MSMA pipeline: sensing, perception, and communication. Their framework allows for the realistic testing of adversarial effects using Monte Carlo analysis and detailed case studies, which highlight crucial system vulnerabilities and inform the development of more robust, security-aware collaborative architectures.

Results and Implications

Case studies conducted using MAST reveal that centralized multi-agent fusion pipelines are particularly susceptible to adversarial manipulations, which can compromise situational awareness. The framework demonstrates that without robust security mechanisms, MSMA architectures remain vulnerable to a broad range of attacks that can disrupt AV operations by introducing false positives or masking genuine threats.

The Monte Carlo evaluations provide an in-depth statistical analysis of the impact of adversary parameters such as the number of compromised agents and false information injected. The findings reinforce the need for developing intelligent integrity checks and robust data association techniques to secure MSMA sensor fusion pipelines.

Future Directions

A critical area identified for future work is the implementation of integrity mechanisms at the command center level to filter adversarial data. Such mechanisms would enhance the resilience of collaborative sensor fusion systems against both detected and undetected malicious interventions.

Additionally, while the current framework incorporates static infrastructure agents with predefined datasets for simplicity, future developments could focus on extending the testbed to include dynamic dataset generation with multiple mobile agents. This expansion would more comprehensively capture real-world AV scenarios.

Conclusion

In summary, MAST represents a significant step towards a more secure integration of collaborative systems in autonomous vehicle applications. By enabling detailed security evaluations of MSMA pipelines in a flexible and scalable manner, MAST not only uncovers existing vulnerabilities but also serves as a foundational tool for driving advancements in the secure deployment of autonomous technologies. Such a testbed is critical for preemptively addressing the threats posed by increasingly sophisticated cyber-attacks on autonomous systems.