Machine Learning in Wavelet Domain for Electromagnetic Emission Based Malware Analysis

You are here

Top Reasons to Join SPS Today!

1. IEEE Signal Processing Magazine
2. Signal Processing Digital Library*
3. Inside Signal Processing Newsletter
4. SPS Resource Center
5. Career advancement & recognition
6. Discounts on conferences and publications
7. Professional networking
8. Communities for students, young professionals, and women
9. Volunteer opportunities
10. Coming soon! PDH/CEU credits
Click here to learn more.

Machine Learning in Wavelet Domain for Electromagnetic Emission Based Malware Analysis

By: 
Nikhil Chawla; Harshit Kumar; Saibal Mukhopadhyay

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 F1  score  in detecting malware and 0.88 F1  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.

SPS on Twitter

  • New SPS Webinar! On Friday, 29 October, join Dr. Jérôme Gilles for "Empirical Wavelets," based on his original arti… https://t.co/ZuZ7qwO9Pc
  • The Brain Space Initiative Talk Series continues on Friday, 29 October when Dr. Selin Aviyente presents "Cross-Freq… https://t.co/Jxgu2soJCc
  • Join the Brain Space Initiative for another virtual mixing event on Wednesday, 27 October! Grab a coffee and meet w… https://t.co/KA3kuPUGw0
  • We're proud to sponsor a new journal, IEEE Transactions on Quantum Engineering, publishing regular, review, and tut… https://t.co/cZskrh9cvX
  • We are now seeking mentors and students for the launch of a new initiative, Mentoring Experiences for Underrepresen… https://t.co/i9SarNyKm9

SPS Videos


Signal Processing in Home Assistants

 


Multimedia Forensics


Careers in Signal Processing             

 


Under the Radar