Interactive Bayesian Generative Models for Abnormality Detection in Vehicular Networks

TL;DR

This paper introduces a Bayesian generative modeling approach for detecting abnormalities in vehicular networks, utilizing multi-modal data and advanced filtering techniques to improve online security and surveillance.

Contribution

It presents a novel combination of Generalized Dynamic Bayesian networks and Interactive Modified Markov Jump Particle filters for real-time abnormality detection in V2X networks.

Findings

High detection probability for network abnormalities

Effective online security monitoring of vehicular links

Accurate prediction of network states and vehicle trajectories

Abstract

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).