Year: 2025
Type: review
Abstract: VANETs are key to intelligent transport, offering significant benefits for safety, efficiency, and self-driving cars. However, the wireless nature of VANETs makes them vulnerable to a variety of attacks. A robust security measure is a mandatory requirement. This paper provides a scientific literature review that examines deep learning (DL) methods applied to Intrusion Detection Systems (IDS) within VANETs. We carefully reviewed recent research, identifying a range of deep learning architectures. Based on our findings, various DL models are implemented to enhance the security of VANETs, including Deep Belief Networks (DBNs), Recurrent Neural Networks (RNNs) like LSTMs and GRUs, and Convolutional Neural Networks (CNNs). The findings consistently show that these DL models achieve higher accuracy in detecting attacks than traditional machine learning models. Their performance is typically evaluated using confusion matrix metrics, accuracy, precision, recall and F1-score. Regardless of their effective, some key challenges were discovered. DL models face challenges like large computational demands, privacy concerns, and a scarcity of quality training data. Also, we explore developing trends and future routes, for example Federated Learning (FL), Software-Defined Networking (SDN), and blockchain integration, which seem capable of addressing VANETs’ challenges. In summary, this review indicates deep learning is crucial to strengthening IDS within VANETs. Implementing these advanced techniques is essential for safe and reliable intelligent transport system
Source: UTUPub (University of Turku)
Author: Bekele, Henok
Language: English
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7111938928