Abstract
ABSTRACTThis study explores advanced methods for community discovery attacks in Vehicular Ad hoc Networks (VANETs) using deep learning techniques. It begins with a comprehensive analysis of VANET characteristics, classifications, routing protocols, and topological structures, followed by an exploration of deep learning applications in network community detection attacks, focusing on state‐of‐the‐art algorithms and evaluation metrics. The core contribution is a novel community discovery attack method for VANETs that integrates graph convolutional neural networks with multi‐head and bidirectional attention mechanisms. The proposed approach is rigorously evaluated through extensive experimentation and in‐depth analysis of results. The study concludes with a critical summary of findings and outlines promising future research directions, aiming to provide innovative strategies for enhancing community discovery attacks in vehicular networks. This research has potential implications for improving communication efficiency, resource allocation, and performance in VANET environments.
Published Version
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call