Radiation emission data

This paper introduces a novel chip electromagnetic fingerprint (EMF) feature extraction scheme based on the Frequency-domain Constellation Trajectory Figure (FCTF), as well as an EMF recognition method using Convolutional Neural Networks (CNN) for MCU function recognition. The FCTF offers a two-dimensional visualization of the amplitude-frequency characteristics of chip electromagnetic (EM) leakage, effectively highlighting high-amplitude frequency features while suppressing low-amplitude ones. By employing CNNs to learn and recognize the distinct functional features of the FCTF, we have developed the FCTF-CNN chip EMF feature recognition approach. Compared with existing chip EMF recognition methods, the FCTF-CNN method not only achieves high recognition accuracy and low computational complexity but also demonstrates unique advantages in resisting Gaussian white noise interference, as confirmed through derivation and validation. Experimental results show that under signal-to-noise ratios (SNR) of -20 dB and 0 dB, the FCTF-CNN method achieves recognition accuracies of 64.98% and 99.965% for the 14 functions of the STM32F103ZET6 MCU, respectively. These results are significantly superior to those of existing MCU EMF recognition methods.