  Embedded devices are omnipresent in modern networks including the ones
operating inside critical environments. However, due to their constrained
nature, novel mechanisms are required to provide external, and non-intrusive
anomaly detection. Among such approaches, one that has gained traction is based
on the analysis of the electromagnetic (EM) signals that get emanated during a
device's operation. However, one of the most neglected challenges of this
approach is the requirement for manually gathering and fingerprinting the
signals that correspond to each execution path of the software/firmware.
Indeed, even simple programs are comprised of hundreds if not thousands of
branches thus, making the fingerprinting stage an extremely time-consuming
process that involves the manual labor of a human specialist. To address this
issue, we propose a framework for generating synthetic EM signals directly from
the machine code. The synthetic signals can be used to train a Machine Learning
based (ML) system for anomaly detection. The main advantage of the proposed
approach is that it completely removes the need for an elaborate and
error-prone fingerprinting stage, thus, dramatically increasing the scalability
of the corresponding protection mechanisms. The experimental evaluations
indicate that our method provides high detection accuracy (above 90% AUC score)
when employed for the detection of injection attacks. Moreover, the proposed
methodology inflicts only a small penalty (-1.3%) in accuracy for the detection
of the injection of as little as four malicious instructions when compared to
the same methods if real signals were to be used.
