Interactive Bayesian Generative Models for Abnormality Detection in Vehicular Networks
arxiv(2024)
摘要
The following paper proposes a novel Vehicle-to-Everything (V2X) network
abnormality detection scheme based on Bayesian generative models for enhanced
network self-awareness functionality at the Base station (BS). In the learning
phase, multi-modal data signals contrived by the vehicles' integrated and
sensing module are imbued into data-driven Generalized Dynamic Bayesian network
(GDBN) models. Following that, during the testing phase, an Interactive
Modified Markov Jump Particle filter (IM-MJPF) is utilized to forecast
forthcoming network states and vehicle trajectories by leveraging the
assimilated semantics embedded in the coupled multi-GDBNs. This approach
involves learning statistically correlated association between evolving
trajectories and network communication links. Security and surveillance of
Internet of Vehicles (IOVs) links are performed online with high detection
probabilities by matching predicted with observed network connectivity maps
(graphs).
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