The Internet of Vehicles (IoV) faces critical cybersecurity challenges that require real-time intrusion detection on resource-constrained edge devices. This study proposes a Three-stage Variance-correlation Binary Grey Wolf Optimization (TVC-BGWO) feature selection framework integrated with ensemble learning. The framework combines Variance Threshold filtering, Correlation-based filtering, and Binary Grey Wolf Optimization to achieve 93.59% feature reduction (78→5 features) while maintaining competitive detection performance. Random Forest with TVC-BGWO achieved 99.91% binary accuracy and 99.87% multiclass accuracy 6 attack types plus benign traffic on Canadian Institute for Cybersecurity Intrusion Detection System (CIC-IDS2017). Statistical validation (McNemar’s x2 = 82.01, p < 0.001) confirms a significant improvement. The framework provides 59.0% training time reduction and sub-millisecond inference latency meeting V2V communication requirements. Adversarial evaluation reveals exceptional resilience to data poisoning 99.98% robustness and moderate vulnerability to test-time perturbations. This work contributes to intelligent transportation security by demonstrating competitive intrusion detection performance while achieving substantial computational efficiency gains suitable for edge deployment.