Securing the Unforeseen: Enhancing VANET Security with Dynamic Honeypots and Attack Rate Analysis

Addressing known threats constitutes the foundational layer of cybersecurity defenses. However, the real challenge emerges in anticipating and mitigating unforeseen attacks. Current security methodologies work well against familiar threats but often struggle with new or unforeseen attacks. This paper examines the Trust Origin within Trust Management Systems (TMS) by linking it to the network attack rate, thereby refining trust assessments and predicting new attacks. Combining Machine Learning (ML) algorithms with honeypots, we offer a comprehensive defense for Vehicular Ad-hoc Networks (VANETs), adept at detecting anticipated and unexpected attacks through attack rate analysis. Our methodology evaluates the network's security status by examining its ability to identify known attacks, referred to as prepared-for attacks. Subsequently, this information serves as a foundation to predict future attacks that still need to be identified, termed unprepared-for attacks. Through extensive testing, we demonstrate the viability of a dual strategy that encompasses the detection of prepared-for attacks and the prediction of unprepared-for ones. Experimental results reveal a significant improvement in predicting unprepared-for attacks, evidenced by enhanced accuracy, precision, and recall. Additionally, we conduct experiments to determine the optimal deployment of honeypots for maximum efficiency.