{'fraction_non_alphanumeric': 0.01685985247629083, 'fraction_numerical': 0.004214963119072708, 'mean_word_length': 4.900621118012422, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Detection of RF devices based on their unintended electromagnetic emissions using Principal Components Analysis

Radio Frequency devices produce Unintended Electromagnetic Emissions (UEEs). These emissions have been found to be unique from device to device due to small differences in the physical components that make up the device. The property of uniqueness of UEE has been used to detect and identify the device producing the emission. However, UEEs are low power signals often buried within the noise band, which makes them difficult to detect. In this paper, we present a novel approach of the application of Principal Component Analysis (PCA) in detecting UEEs. UEE samples are collected from two RF devices at three different distances of 3 feet, 6 feet and 10 feet using spectrum analyzer. Our approach can detect if these low power signals are UEEs or noise. A decision table based on PCA parameters to detect UEE signals is also proposed.