Defense Mechanisms for Vehicular Networks: Deep Learning Approaches for Detecting DDoS Attacks

Abstract

Vehicular Ad-hoc Networks (VANETs) are engineered to meet the distinctive demands of vehicular communication, facilitating interactions between vehicles and roadside infrastructure to enhance road safety, traffic efficiency, and diverse applications such as traffic management and infotainment services. However, the looming threat of Distributed Denial of Service (DDoS) attacks in VANETs poses a significant challenge, potentially disrupting critical services and compromising user safety. To address this challenge, this study proposes a novel deep learning (DL)-based model that integrates Long Short-Term Memory (LSTM) architecture with self-attention mechanisms to effectively detect DDoS attacks in VANETs. By incorporating autoencoders for feature extraction, the model leverages the sequential nature of VANET data, prioritizing relevant information within input sequences to accurately identify malicious activities. With an impressive accuracy of 98.39%, precision of 97.79%, recall of 98.00%, and F1-score of 98.20%, the proposed approach demonstrates remarkable efficacy in safeguarding VANETs against cyber threats, thereby contributing to enhanced road safety and network reliability.