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14 June 2023 Deep neural networks for detecting anomalies in unintended radiated emissions
Kenneth L. Witham, Connor Robb, Dave Tahmoush
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Proceedings Volume 12549, Unmanned Systems Technology XXV; 125490T (2023) https://doi.org/10.1117/12.2666271
Event: SPIE Defense + Commercial Sensing, 2023, Orlando, Florida, United States
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Abstract
Unintended Radiated Emissions (URE) are emitted by all electrical devices and can be analyzed to determine changes in state on the emitting device. This paper aims to analyze UREs from a target device and classify if the device has changed operating states given a new measurement of the UREs. The UREs for a Raspberry Pi in different operating states are collected and analyzed. We detect if the target device changes operating states using a recurrent neural network to predict the URE spectrum power given multiple previous URE measurements. The predicted emissions are then compared to the measured emissions and a deep neural network classifies the measured emissions as a state change. Multiple other model types are compared including statistical classifiers and more complex machine learning models and our proposed model is found to perform the best in our dataset. We achieved an accuracy and F1-score of 90% in our real-world dataset.
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Kenneth L. Witham, Connor Robb, and Dave Tahmoush "Deep neural networks for detecting anomalies in unintended radiated emissions", Proc. SPIE 12549, Unmanned Systems Technology XXV, 125490T (14 June 2023); https://doi.org/10.1117/12.2666271
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KEYWORDS
Machine learning
Neural networks
Autoregressive models
Feature extraction
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