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Modern heavy vehicles rely on insecure protocols (CAN and SAE-J1939) to facilitate communication between the embedded devices that control their various subsys- tems. 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 computa- tional and storage burden of the model, we employ 8-bit Quantization-Aware Training (QAT), enabling its deploy- ment on resource-constrained embedded devices while maintaining high performance. We evaluate Transformer and LSTM-based architectures, demonstrating that both effectively reconstruct signals with minimal computa- tional and storage overhead. Our approach achieves sig- nal 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.