Bibliographic Details
| Title: |
Security-Aware Sensor Fusion with MATE: the Multi-Agent Trust Estimator |
| Authors: |
Hallyburton, R. Spencer, Pajic, Miroslav |
| Publication Year: |
2025 |
| Collection: |
Computer Science |
| Subject Terms: |
Computer Science - Cryptography and Security, Electrical Engineering and Systems Science - Systems and Control |
| More Details: |
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. |
| Document Type: |
Working Paper |
| Access URL: |
http://arxiv.org/abs/2503.04954 |
| Accession Number: |
edsarx.2503.04954 |
| Database: |
arXiv |
