Lightweight Deep Learning for Cyber-Resilient Heavy Vehicles: Efficient Signal Reconstruction on Embedded Systems

Modern heavy vehicles rely on insecure protocols (CAN and SAE-J1939) to facilitate communication between the embedded devices that control their various subsystems. Due to the growing integration of wireless-enabled embedded devices, vehicles are becoming increasingly vulnerable to remote cyberattacks against their embedded networks. We propose an efficient deep-learning-based approach for mitigating such attacks through real-time J1939 signal reconstruction. Our approach uses random feature masking during training to build a generalized model of a vehicle's network. To reduce the computational and storage burden of the model, we employ 8-bit Quantization-Aware Training (QAT), enabling its deployment on resource-constrained embedded devices while maintaining high performance. We evaluate Transformer and LSTM-based architectures, demonstrating that both effectively reconstruct signals with minimal computational and storage overhead. Our approach achieves signal reconstruction with error levels below 1% of their operating range while maintaining a very low storage footprint of under 1 MB, demonstrating that lightweight deep-learning models can enhance resiliency against real-time attacks in heavy vehicles.