Security-Aware Sensor Fusion with MATE: the Multi-Agent Trust Estimator

• URL: http://arxiv.org/abs/2503.04954v1
• Date: Thu, 06 Mar 2025 20:33:25 GMT
• Title: Security-Aware Sensor Fusion with MATE: the Multi-Agent Trust Estimator
• Authors: R. Spencer Hallyburton, Miroslav Pajic,
• Abstract summary: Lacking security awareness, sensor fusion in systems with multi-agent networks such as smart cities is vulnerable to attacks.<n>We design security-aware sensor fusion that is based on the estimates of distributions over trust.<n>A mix of novel and classical security-relevant metrics show that our security-aware fusion enables building trustworthy situational awareness even in hostile conditions.
• Score: 11.246557832016238
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Lacking security awareness, sensor fusion in systems with multi-agent networks such as smart cities is vulnerable to attacks. To guard against recent threats, we design security-aware sensor fusion that is based on the estimates of distributions over trust. Trust estimation can be cast as a hidden Markov model, and we solve it by mapping sensor data to trust pseudomeasurements (PSMs) that recursively update trust posteriors in a Bayesian context. Trust then feeds sensor fusion to facilitate trust-weighted updates to situational awareness. Essential to security-awareness are a novel field of view estimator, logic to map sensor data into PSMs, and the derivation of efficient Bayesian updates. We evaluate security-aware fusion under attacks on agents using case studies and Monte Carlo simulation in the physics-based Unreal Engine simulator, CARLA. A mix of novel and classical security-relevant metrics show that our security-aware fusion enables building trustworthy situational awareness even in hostile conditions.

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