Cascaded Deep Ensemble Learning for Position Integrity Assessment in Vehicular Ad Hoc Networks

Position integrity is a critical requirement for the safe operation of vehicles in Intelligent Transportation Systems (ITS). Manipulated position messages lead to unsafe decisions and traffic confusion and create possible accidents. Existing approaches primarily focus on improving localisation accuracy and securing vehicle identities, rather than detecting false positional behaviors. Also, real-world communication challenges make integrity verification more difficult in dense vehicular environments. To address these challenges, we propose a Cascaded-Deep Ensemble Learning (CADE) model to assess positional integrity in VANET environments. The CADE model evaluates the trust of shared position information using a multi-stage pipeline. It combines motion-prediction-based Position Error (PE) estimation, adaptive safety-bound evaluation via Adaptive Dynamic Level (ADL), temporal-behavior verification using Temporal Consistency Score (TCS), and cooperative validation via Cooperative Plausibility Index (CPI). These integrity indicators are fused and evaluated using lightweight ensemble classifiers to obtain the position information of active vehicles. The proposed CADE framework is evaluated on the VeReMi dataset; experimental results show that the approach achieves a high detection accuracy of 99.50% and demonstrates a strong ability to identify false-position attacks even under congested vehicular communication conditions.