Work

IntentProv-IoV: Causally Grounded Provenance for Traffic-Intent Preservation in Explainable Vehicular Security

Year: 2026

Type: article

Abstract: Internet of Vehicles (IoV) security mechanisms often classify isolated messages or assign node-level trust scores, yet these decisions do not explain whether a malicious but authenticated event has distorted the intended evolution of traffic. This paper proposes IntentProv-IoV, a causally grounded provenance framework for traffic-intent preservation in V2X environments. Traffic intent is modeled as the short-horizon collective state expected under non-adversarial conditions, and deviation is measured between predicted and observed traffic states. The framework constructs temporal provenance graphs linking vehicles, roadside units (RSUs), cooperative perception outputs, prediction nodes, and traffic-control decisions. To remove the ambiguity of marginal contribution, node contribution is formalized as an interventional effect in a structural causal model and estimated through Monte Carlo counterfactual edge-weight attenuation, with a linear sensitivity fallback for real-time edge deployment. A calibrated composite score integrates anomaly evidence, traffic-intent deviation, trust risk, and provenance contribution. The evaluation design compares IntentProv-IoV with detection, trust, blockchain trust, graph anomaly, Granger causal, structural causal, and counterfactual GNN baselines and includes predictor sensitivity, adaptive adversaries, prediction noise, packet loss, trajectory-only real-data validation, and edge overhead. Simulation-scale results indicate improved attribution precision, stronger traffic-intent deviation reduction, and edge-suitable latency. By shifting V2X security from message-level detection to causally explainable traffic-intent assurance, IntentProv-IoV provides a more accountable security objective for cooperative vehicular systems.

Source: Symmetry

DOI: 10.3390/sym18091430

Author: Eman Abouelkheir

Institution: Qassim University

Language: English

Topic: Vehicular Ad Hoc Networks (VANETs)

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Industry, innovation and infrastructure, Sustainable cities and communities, Peace, justice, and strong institutions

Open Access status: gold

Estimated APC paid: $2,896