The Controller Area Network (CAN) is a widely used communication protocol in automobiles, but it is vulnerable to various types of attacks. To address this issue, researchers have been exploring the use of intrusion detection systems (IDS) for the CAN bus. Deep learning and machine learning have been proven to be powerful tools for detecting intrusions accurately and quickly. However, deep learning models require large amounts of data to achieve optimal performance, which can be challenging in the case of a CAN bus IDS. To overcome this challenge, we propose a novel machine learning-based IDS called CANPerFL that uses a personalized federated learning scheme to aggregate datasets from different car models. By building a universal model trained on a small amount of data from each manufacturer, we can provide global knowledge that can be transferred to improve the performance of each participant. To demonstrate the efficiency of the proposed model, we collected a real CAN dataset consisting of three different car models: KIA, BMW, and Tesla. The experimental results show that the proposed model increases F1 scores by 4% overall, compared to baselines. Moreover, the proposed system provides significant advantages when the local dataset of each participant is relatively small. According to our experiments, the proposed models can achieve F1 scores of more than 90% with at least 30k training samples on each client. Finally, we show empirically that each participant takes benefits from joining the CANPerFL system.

Many studies have focused on obtaining high accuracy in the design of Intrusion Detection Systems (IDS) for in-vehicle networks, neglecting the significance of different intensive packet injection techniques. Because of their reliance on scenario-specific training datasets, these IDSs are vulnerable to failing to detect real-world attacks. This study implemented deep learning (DL)–based classification for intrusion detection using a Gated Recurrent Unit (GRU) while considering various intrusion frequencies. Different intrusion frequencies are comprehensively addressed with frequency-agnostic intrusion and resolved by generalizing features for DL input through time series segmentation and frequency domain conversion using Gabor filtering. For training purposes, five types of vehicle data are used, encompassing DoS, fuzzing, and replay attack scenarios. The accuracy range for mechanical version vehicles is typically between 95% and 100%. For electronic vehicles, it is around 90%. Considering the nature of this IDS system, it has been named a Comprehensive Frequency-Agnostic Intrusion Detection System (CF-AIDS). Although this IDS can perform better in all aspects, achieving more efficient results requires a larger amount of situational data.

Modern vehicles are increasingly vulnerable to cyber-attacks due to the lack of encryption and authentication in the Controller Area Network, which coordinates communication between Electronic Control Units. This study investigates the use of Recurrent Neural Networks to improve the accuracy and efficiency of Intrusion Detection Systems in vehicular networks. Focusing on sequential CAN data, we compare the performance of different RNN architectures, including SimpleRNN, LSTM, and GRU, in detecting common attack types like Denial-of-Service, Fuzzing, Replay, and Malfunction. Sixty-three RNN models were tested with various hyperparameters, including optimizers and learning rates. Our findings indicate that GRU models achieve superior detection performance, particularly in resource-constrained environments, offering near 99% accuracy in identifying cyber threats. The study also explores the implications of six different hardware choices, revealing that devices like Jetson and Raspberry Pi, when paired with optimal hyperparameters, can deliver efficient real-time IDS performance at a lower cost. These results contribute to the ongoing effort to secure vehicular communication systems and highlight the importance of balancing accuracy, resource usage, and system cost in IDS deployment.