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Vehicular ad-hoc networks (VANET) technology, which is an open-access network, renders a quick, simple to deploy, and cheap solution for intelligent traffic control as well as traffic disaster preventive measure but it is prone to disparate sorts of attacks. The Sybil attack (SA) is the most harmful attacks that the VANET has to face. In this, the attacker generates manifold identities to fake manifold nodes. It is extremely onerous to defend as well as detect, especially if it is commenced by means of some connived attackers utilizing their genuine identities. Here, a deep learning-centered intrusion detections system (IDS) is proposed utilizing CMEHA-DNN for detecting the SA in VANET. The proposed technique encompasses ‘4’ steps: (i) cluster formation (CF), (ii) cluster head (CH) selection, (iii) attack detection, and (iv) security of VANET. Initially, the MKHM clustering algorithm clusters the vehicles. Next, the Floyd …