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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. 

Modern network infrastructures are in a constant state of transformation, in large part due to the exponential growth of Internet of Things (IoT) devices. The unique properties of IoT-connected networks, such as heterogeneity and non-standardized protocol, have created critical security holes and network mismanagement. In this paper we propose a new measurement tool, Intrinsic Dimensionality (ID), to aid in analyzing and classifying network traffic. A proxy for dataset complexity, ID can be used to understand the network as a whole, aiding in tasks such as network management and provisioning. We use ID to evaluate several modern network datasets empirically. Showing that, for network and device-level data, generated using IoT methodologies, the ID of the data fits into a low dimensional representation. Additionally we explore network data complexity at the sample level using Local Intrinsic Dimensionality (LID) and propose a novel unsupervised intrusion detection technique, the Weighted Hamming LID Estimator. We show that the algortihm performs better on IoT network datasets than the Autoencoder, KNN, and Isolation Forests. Finally, we propose the use of synthetic data as an additional tool for both network data measurement as well as intrusion detection. Synthetically generated data can aid in building a more robust network dataset, while also helping in downstream tasks such as machine learning based intrusion detection models. We explore the effects of synthetic data on ID measurements, as well as its role in intrusion detection systems.