SPS Feed

This paper presents a signal processing and machine learning (ML) based methodology to leverage Electromagnetic (EM) emissions from an embedded device to remotely detect a malicious application running on the device and classify the application into a malware family. We develop Fast Fourier Transform (FFT) based feature extraction followed by Support Vector Machine (SVM) and Random Forest (RF) based ML models to detect a malware. We further propose methods to learn characteristic behavior of different malwares from EM traces to reveal similarities to known malware families and improve efficiency of malware analysis.

Tomography has been widely used in many fields. The theoretical basis of tomography is the Radon transform, which is the line integral along a radial line oriented at a specific angle. In practice, the detector that collects the projection has a certain width, which does not coincide with the line integral. Therefore, the resolution of the reconstructed image will be reduced. In order to overcome the effect of the detector width on the reconstruction quality, some reconstruction methods have taken the influence of the detector width into account and have achieved high reconstruction quality, such as the distance-driven model (DDM) and the area integral model (AIM).