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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. We propose to use Discrete Wavelet Transform (DWT) based feature extraction from spectrograms of EM side-channel traces and perform ML on the extracted features to learn fine-grained patterns of malware families. The experimental demonstration on Open-Q 820 development platform demonstrate 0.99 F 1 score in detecting malware and 0.88 F 1 score in uniquely classifying malwares among 8 malware family evaluated using Support Vector Machines (SVM) and Random Forest (RF) Machine Learning(ML) models. We also demonstrate capability of proposed framework in identifying new unknown applications with 0.99 recall and unknown malware family with 0.87 recall.
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