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Security and safety are critical concerns in Vehicular Adhoc Networks.  vulnerable to Distributed Denial of Service (DDoS) attacks, which occur when multiple vehicles carry out various tasks. This cause disrupts the normal functioning of legitimate routes. In this work, the Hybrid PSO-BAT Optimization Algorithm (HBPSO) Algorithm based on modified chaos -cellular neural network (Chaos - CNN) approaches has been proposed to overcome DDoS attacks. The suggest approaches consists of three-part which are hybrid optimization search algorithm to enhance the route from source to destination, chaos theory module is used to detect the abnormal nodes, then on Modified Chaotic CNN (MCCN) employed to prevent a malicious node from sending data to the destination by determining node that consumer more resource, packets lose or the victim could reset the path between the attacker and itself. CICIDS dataset has been used to test and evaluate the performance of the proposed approach based on the criteria of accuracy, packet loss, and jitter.  The Chaos - CNN approached results to outperform similar models of the related work and the approach protects the VANETs with high accuracy of 0.8736, specificity of 0.9959, TPR of 0.9561, and FPR of 0.78, Detection rate 0.9561.

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