Improved IDS for Vehicular Ad-Hoc Network using Deep Learning Approaches

In the contemporary landscape of transportation, Vehicular Adhoc networks (VANETs) have emerged as a pivotal infrastructure, facilitating real-time communication among vehicles and infrastructure to enhance road safety and transportation efficiency. However, the dynamic and open nature of VANETs exposes them to diverse security threats, necessitating robust security measures. Intrusion Detection Systems (IDS) are fundamental for safeguarding VANETs, but their effectiveness in identifying and mitigating intrusions, particularly with evolving attack strategies, remains a challenge. This research focuses on enhancing IDS for VANETs by deploying Convolutional Neural Networks (CNN), a subset of deep learning techniques known for their proficiency in image and signal processing, in intrusion detection. The background underscores the significance of VANETs in modern transportation, emphasizing the growing need for more robust security mechanisms due to increasing connectivity and the advent of Intelligent Transportation Systems (ITS). The primary objective of this research is to assess the performance of CNN-based IDS in the VANET context, specifically in their ability to detect and respond to intrusions effectively and efficiently. The contribution of this research lies in its empirical evaluation of CNN-based IDS for VANETs, offering practical insights into the implementation of advanced security measures in vehicular communication networks. By bridging the gap between theory and application, this research serves as a valuable reference for researchers, practitioners, and policymakers, aiding in the establishment of resilient and secure vehicular communication networks in the era of growing connectivity and sophisticated security challenges.