Enhanced Machine Learning Based Techniques for Security in Vehicular Ad-Hoc Networks

Abstract

Vehicular Ad-Hoc Networks, also referred as VANETs, have emerged as an interesting area of research as a result of ever significant increase in the number of automobiles on roads. Built - in safety smart vehicular traffic infrastructure protects both passengers and drivers, but due to its dynamic nature, real-time implementation is difficult. They lay the groundwork for the creation of intelligent transportation systems (IPS) and frameworks, which allow road entities to communicate with one another and build new applications and services with the goal of improving both the driving experience and overall road safety. The demanding features of VANETs make it difficult to establish security measures, resulting in gaps that attackers could exploit. This research provides smart structured protective systems for VANETs that use machine learning (ML) based algorithms. The enhanced ML algorithms improves attack detection, protecting data inter-communications between various sources and destinations, and ensuring strong anonymity, authentication, and privacy. The proposed system trains and tests its machine learning algorithms on publicly available datasets of vehicle communications. As a result, the outcomes are repeatable and verifiable. The machine learning-based security system can detect attacks while maintaining low False Positive Rate values (FPR). The findings also suggest that the framework may benefit from employing a variety of algorithms present at various hierarchical levels, selecting algorithms with high performance and focus at the cost of preciseness in lower levels and additionally sophisticated, detailed, and accurate algorithms present in top levels.