AC-NB: Ant colony-based secure routing and naive bayes detection of black hole attacks in VANETs

Vehicle Ad-hoc Networks (VANETs) are fundamental to achieving zero-fatality intelligent transportation, yet their open nature exposes them to severe security threats, particularly blackhole attacks that disrupt routing and compromise safety. Existing countermeasures force a trade-off between security and efficiency: cryptographic methods impose excessive overhead, while machine learning detectors lack real-time adaptability. To bridge this gap, this paper proposes AC-NB, a closed-loop framework that integrates an enhanced Ant Colony Optimization (ACO) routing protocol with a lightweight Naïve Bayes classifier. In AC-NB, a continuous feedback cycle is established: the Bayesian detector computes node trust values from packet drop rate, forward rate, and RSSI mismatch, and these trust values directly update the ACO heuristic—which jointly considers link security, link quality, distance, and node trust—steering route selection away from malicious nodes. Extensive highway simulations under 20% blackhole attackers show that AC-NB achieves end-to-end delays of 13–48 ms (an order-of-magnitude reduction over baselines), packet delivery ratio between 80% and 98%, throughput between 78% and 98%, and detection rates above 90% with low false positives. These results confirm that tightly coupling probabilistic detection with adaptive multi-criteria routing can simultaneously deliver low latency and robust security, offering a practical foundation for safe autonomous driving.