Real-time communication between autonomous vehicles, infrastructure, and their environment has facilitated the Internet of Vehicles (IoVs). Although this connectivity provides vehicular networks with significant benefits, it also introduces severe security threats, such as message injection attacks, particularly due to the heavy reliance on the Controller Area Network (CAN) protocol, which is inherently vulnerable. Electronic Control Units (ECUs) become primary targets for these attacks, leading to unsafe vehicle behaviors. To address these challenges, an Intrusion Detection System (IDS) based on deep learning architectures, Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), and... More >

The Internet of Vehicles (IoV) has greatly influenced transportation by allowing autonomous vehicles to interact and communicate with other cars as well as with the surrounding traffic system. Even so, being interconnected comes with risks in terms of cyber attacks, for example, by injecting messages or fooling sensors through CAN systems. The study, consequently, suggests an Intrusion Detection System (IDS) that uses Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), Bidirectional Encoder Representations from Transformers (BERT), and RoBERTa, to properly detect and handle these cyber threats. To solve the problem of unbalanced data, we use R... More >