Ensuring secure and reliable communication in vehicular networks, a type of mobile ad hoc network is a major challenge due to the frequently changing topology. The dynamic nature of VANETs, where nodes can frequently join or leave the network, can lead to insecure communication and link disconnections caused by obstructions such as buildings, tunnels, bridges, and fossils. Disconnections can cause packet loss and negatively impact network performance. It is often difficult to determine the reason behind packet loss, whether it is due to node detection or security threats. VANETs are vulnerable to various attacks, including denial-of-service, black hole, and greyhole attacks. This study focuses on mitigating DoS flooding attacks to enhance network safety and efficiency of VANET communication. In this work, we propose a novel intrusion detection algorithm IS-DOS, which may help effectively identify and prevent DOS flooding attacks by leveraging the traffic pattern analysis with the AODV routing protocol framework. To evaluate the effectiveness of the proposed approach, use network simulator NS 2.34 for the conventional DOS, QRT-DOS and our proposed model IS-DOS. The quantitative performance metrics such as packet delivery ratio, throughput, end-to end delay, and routing overhead were analyzed under various network conditions. The simulation results demonstrated that IS-DOS improves the packet delivery ratio by 15% and throughput by 12% compared to existing DOS and QRT-DOS mitigation approach. Moreover, IS-DoS diminishes average packet delay by 20% and drops routing overhead by 18%, signifying enhanced network efficiency and resilience. These findings recommend that IS-DoS not solitary mitigates flooding attacks more effectually than prior approaches but also sustains network stability in the existence of high mobility and recurrent topology changes. The novelty of this work deceits in its adaptive detection mechanism tailored for VANET surroundings, which develop beyond existing solutions by suggesting real-time, low-overhead attack anticipation without compromising normal network operations. This research contributes to improving the security and reliability of vehicular communications, which is indispensable for the prevalent adoption of intelligent transportation systems.