The increasing integration of smart and autonomous functionalities in modern vehicles has introduced new cybersecurity challenges, particularly concerning vehicular communication and control systems. This paper proposes a deep learning-based intrusion detection system designed to identify and classify cyberattacks in smart cars in real-time. The system leverages data collected from the vehicle’s CAN bus, sensors, and communication modules to detect anomalies associated with common attack types such as data spoofing, denial-of-service (DoS), signal jamming, and man-in-the-middle (MitM) intrusions. Three deep learning models—Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and Long Short-Term Memory networks (LSTM)—are implemented and evaluated on a labeled dataset. The experimental results demonstrate that the LSTM model outperforms CNN and RNN in terms of accuracy (98.1%) and F1-score (97.9%), making it particularly effective for detecting sequential and time-dependent attacks. The proposed system exhibits strong adaptability, low detection latency, and potential for real-time deployment in connected vehicle environments, thereby contributing to the development of secure intelligent transportation systems.
A Thu, study studied this question.