We use machine learning to characterize the state of digital devices based on their analog emissions. As digital devices operate, they emit internal information into a number of analog side channels. Remote sensing of these unintended signals leads to low signal-to-noise-ratio (SNR) and significant clutter. We developed classifiers to determine which program is executing on a digital device based on analog radio-frequency (RF) emissions collected via a 500-MHz Riscure RF probe. A standard algorithm was developed to serve as a baseline program and intrusions were simulated by introducing minor modifications to this program. We collected a thousand RF traces from each of these modified programs running on ten different devices for thousands of instruction cycles. The ten devices tested are representative of the Internet of Things (IoT) devices including Arduino Unos and PIC24 processors. Our primary approach to mitigating the impact of low SNR is to extend the program execution and signal collection time. Collecting a training set with more traces than samples is not practical. Even after down-sampling the raw data to thirty samples per instruction, the number of samples exceeds the number of traces by orders of magnitude. Such a training set nearly guarantees overlearning. To mitigate this, we present our Whitened Mean Classifier as a method to whiten this sparse training set and avoid overlearning. Classification accuracy exceeded 90% for the modified programs on a subset of the ten devices.
Ronald A. Riley, James T. Graham, Peter D. Swartz, Rusty O. Baldwin, and Ashwin Fisher, " Classification of low-SNR side channels," Proc. SPIE 10630, Cyber Sensing 2018, 106300B (Presented at SPIE Defense + Security: April 17, 2018; Published: 3 May 2018); https://doi.org/10.1117/12.2304461.
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