Zero‐Tuned Peripheral Stacked Ensemble Model for Enhanced Attack Detection and Mitigation in Next‐Generation Vehicular Networks

ABSTRACT The quick growth of the Internet of Vehicles (IoV) needs secure, low‐latency, and trusted communication frameworks. Existing Intrusion Detection Systems (IDS) are struggling to provide effective threat detection in changing vehicular networks. This creates a critical need for adaptive detection mechanisms that can perform efficiently at the network edge. According to this background, we propose a Zero‐Tuned Peripheral Stacked Ensemble (ZTPSE) for efficient attack detection and mitigation in practical VANET environments. In this framework, lightweight base learners run on edge nodes to detect attacks locally. A central meta‐learner then combines these local outputs using stacking without manual tuning. This design allows fast and efficient detection under different traffic densities and attack scenarios. By combining distributed detection with ensemble learning, ZTPSE detects multiple attack types and allows the system to take a rapid response action. Simulation results indicate that the proposed ZTPSE framework achieves high detection accuracy of 95.70% in varying traffic densities and maintains a low false‐positive rate (FPR) of 0.05%.