Adaptive RNN Hyperparameter Tuning for Optimized IDS Across Platforms

Abstract

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.