A Multi-Agent Security Testbed for the Analysis of Attacks and Defenses in Collaborative Sensor Fusion
CoRR(2024)
摘要
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.
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