## 3 Intelligent Cluster-Based Routing Approach
### Cluster Formation
Figure 17 illustrates how network latencies of different intensities show a significant decrease, improving the effectiveness of cooperative results. ## 5 Conclusions, Limitations, and Future Directions

The proposed methodology was embraced to address issues regarding malicious nodes, which often lead to packet drops due to their security activities. Additionally, this approach encompassed the task of forecasting and identifying optimal routes, effectively employing a modified version of the AODV routing protocol. An intelligent cluster-based routing strategy was harnessed to heighten routing efficiency within the network. Previously, detecting malicious nodes rested with RSUs, which employed an ANN classifier to scrutinize nodes' behavior and discern suspicious activities. Following the establishment

Figure 17: The average delay of the proposed technique was compared to MLCP, RPSS, CCMP, and PCZS + PCNS [1]. of secure clusters, the modified AODV protocol takes center stage, facilitating the dynamic formation of on-demand paths for packet transmission. This intricate protocol leverages forward and backward control packets during route discovery and employs objective functions to meticulously determine the most optimal route. The results obtained from this methodology were judiciously compared with pre-existing routing techniques. The comparative analysis illustrated the efficiency of our proposed approach, which notably excelled in average hop count, end-to-end delay, and network throughput. The main limitations of the research are as follows:

* This study was confined to identifying black hole attacks within the VANET environment. * It is worth noting that neural networks are susceptible to adversarial attacks, which can potentially induce erroneous results within the network. This vulnerability becomes particularly pronounced in vehicular ad hoc networks, where malicious nodes can readily manipulate data. * Detecting black hole attacks primarily centers on routing aspects but is delimited to the research dimensions concerning network load balancing and its intricacies. * While proficient in identifying the shortest, most secure, and efficient routes from source to destination nodes, the research was limited to facilitating confidential communications exclusively among vehicles engaged in specialized convoy operations, which have implications for national security. Using the reinforcement-learning model, our proposed technique will be refined by adapting secure V2V communication with minimum delay. Conceptualization, M.u.H. and A.A.A.-A. ; methodology, A.A.; software, S.; validation, M.M.I., A.A. and M.A. ; formal analysis, J.K.; investigation, Y.A.A.A. ; resources, A.A.; data curation, M.u.H. ; writing--original draft preparation, A.A.; writing--review and editing, A.A.A.-A. ; visualization, J.K.; supervision, Y.A.A.A. ; project administration, M.M.I. ; funding acquisition, M.u.H., A.A.A.-A. and Y.A.A.A. All authors have read and agreed to the published version of the manuscript. This research was funded by the Deanship of Scientific Research at Najran University under the Distinguished Research Funding Program grant code NU/DRP/SERC/12/1. Data Availability StatementData will be available on the request through proper institutional email. The authors thank the Deanship of Scientific Research at Najran University for funding this work under the Distinguished Research Funding Program grant code NU/DRP/SERC/12/1. The authors declare no conflicts of interest. ## References

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