PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.
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.
Kenneth L. Witham,Connor Robb, andDave 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
ACCESS THE FULL ARTICLE
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.
The alert did not successfully save. Please try again later.
Kenneth L. Witham, Connor Robb, 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