Year: 2025
Type: conference-paper
Abstract: The advancement of Intelligent Transport Systems (ITS) and the Internet of Vehicles (IoV) enhances road safety and traveler comfort by enabling safer, more efficient transportation networks. However, these technologies are vulnerable to a range of security threats that malicious actors could exploit. One of the most severe threats to IoV is the Distributed Denial of Service (DDoS) attack, which could disrupt traffic flow, disable vehicular communication, or even cause accidents. This paper proposes a novel Deep Multimodal Learning (DML) approach to detect DDoS attacks in IoV, strengthening cybersecurity in intelligent transport systems. Our DML model integrates Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) networks, enhanced by Attention and Gating mechanisms, alongside a Multi-Layer Perceptron (MLP) with a multimodal intermediate fusion architecture. This innovative method leverages the Framework for Misbehavior Detection (F2MD) to generate a synthetic dataset and deploy our model, enabling real-time DDoS detection and mitigation while overcoming the limitations of the Vehicular Reference Misbehavior (VeReMi) dataset. The proposed approach is evaluated in real-time across various simulated real-world scenarios with differing attacker densities. Our DML model achieves an average accuracy of$\mathbf{9 6. 6 3 \%}$, outperforming classical Machine Learning (ML) and state-of-the-art approaches, demonstrating significant efficacy and reliability in safeguarding vehicular networks against malicious cyberattacks.
Institutions: École Supérieure en Informatique de Sidi Bel Abbes, Université Le Havre Normandie, Laboratoire d'Informatique, du Traitement de l'Information et des Systèmes, Université de Rouen Normandie, Institut National des Sciences Appliquées Rouen Normandie, Centre National de la Recherche Scientifique, École Nationale Supérieure d'Ingénieurs de Caen, Normandie Université, GREYC, Université de Caen Normandie
Language: English
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4414539577