  We present a comprehensive evaluation of the robustness and explainability of
ResNet-like models in the context of Unintended Radiated Emission (URE)
classification and suggest a new approach leveraging Neural Stochastic
Differential Equations (SDEs) to address identified limitations. We provide an
empirical demonstration of the fragility of ResNet-like models to Gaussian
noise perturbations, where the model performance deteriorates sharply and its
F1-score drops to near insignificance at 0.008 with a Gaussian noise of only
0.5 standard deviation. We also highlight a concerning discrepancy where the
explanations provided by ResNet-like models do not reflect the inherent
periodicity in the input data, a crucial attribute in URE detection from stable
devices. In response to these findings, we propose a novel application of
Neural SDEs to build models for URE classification that are not only robust to
noise but also provide more meaningful and intuitive explanations. Neural SDE
models maintain a high F1-score of 0.93 even when exposed to Gaussian noise
with a standard deviation of 0.5, demonstrating superior resilience to ResNet
models. Neural SDE models successfully recover the time-invariant or periodic
horizontal bands from the input data, a feature that was conspicuously missing
in the explanations generated by ResNet-like models. This advancement presents
a small but significant step in the development of robust and interpretable
models for real-world URE applications where data is inherently noisy and
assurance arguments demand interpretable machine learning predictions.
