Vehicular Ad Hoc Networks (VANETs) are considered extremely important section of intelligent transportation system because they enable communication between infrastructure and vehicles for enhancing road safety, and efficiency. As the connectivity level increases between vehicle and infrastructure, then networks are also exposed to growing number of sophisticated cyberattacks particularly zero-day attacks that pose significant threat. The use of Traditional Machine Learning (ML) models can only detect known cybersecurity attacks and face problems in identifying unseen and novel threats. Based on this, the research will explore in detail about the potential of GenAI models like variational autoencoders, generative adversarial networks, and diffusion models for detecting unseen cyberattacks in vehicular networks. A detail comparison of GenAI models and traditional ML was conducted by focusing on detection rates, accuracy, robustness to zero-day attacks, and false positives and negatives. The results showed that Hybrid models by combining ML and GenAI outperformed standalone approaches and achieve high resilience and accuracy. Besides vital advantages of GenAI, some challenges are also discussed in detail like data availability, computational complexity, and adversarial vulnerability. The study concludes through outlining the contribution, limitations, and future research work for enhancing cybersecurity in VANETs through GenAI models.
Call Number
LE3 .A278 2025
Date Issued
2025
Supervisor
Degree Name
Master of Science
Degree Level
Masters
Degree Discipline
Affiliation
Abstract
Publisher
Acadia University