Optimization techniques for IDS-Generated traffic congestion control in VANET

Vehicular Ad-hoc Network (VANET) is an emerging field of wireless networks that enables a variety of vehicle safety and convenience applications. It employs Intrusion Detection System (IDS) frameworks in its different tiers to ensure reliable and secure communication among nodes. However, IDS requires a significant amount of data to process for monitoring intrusive activities in the network. As a result, the volume of traffic increases, resulting in the network congestion. Motivated by this fact, this study provides an overview of the optimization techniques for VANET traffic congestion control. It discusses a state-of-the-art analysis along with the requirements for IDS-generated traffic congestion control. It highlights the congestion control approaches for the traffic generated by an IDS and identifies the challenges in this domain. This study also proposes a novel IDS framework for reducing IDS-generated network traffic by combining the Local Outlier Factor and Random Forest classifier. The proposed study achieved a high precision while yielding low false positive and false negative rates. The study outperformed the existing studies with an increase in accuracy of 1.16% and a reduction in attack detection time of 1.1869 seconds. Additionally, it discusses the possible future research directions that can be applied to address the issues of IDS-generated traffic congestion. Overall, this study serves as a comprehensive guide to the current status of IDS-generated traffic congestion control and diverse approaches to lessen it that can be employed by academicians and researchers.