Abstract
Today, it is possible to perform external anomaly detection by analyzing the involuntary EM emanations of digital device components. However, one of the most important challenges of these methods is the manual collection of EM signals for fingerprinting. Indeed, this procedure must be conducted by a human expert and requires high precision. In this work, we introduce a framework that alleviates this requirement by relying on synthetic EM signals that have been generated from assembly code. The signals are produced with the use of a Generative Adversarial Network (GAN) model. Experimentally, we identify that the synthetic EM signals are extremely similar to the real and thus, can be used for training anomaly detection models effectively. Through experimental assessments, we prove that the anomaly detection models are capable of recognizing even minute alterations to the code with high accuracy.