Microsoft and Intel project converts malware into images before analyzing it

guest · May 10, 2020

Microsoft and Intel project converts malware into images before analyzing it
Microsoft and Intel Labs work on STAMINA, a new deep learning approach for detecting and classifying malware
May 11, 2020
https://www.zdnet.com/article/micro...erts-malware-into-images-before-analyzing-it/

Microsoft and Intel have recently collaborated on a new research project that explored a new approach to detecting and classifying malware.

Called STAMINA (STAtic Malware-as-Image Network Analysis), the project relies on a new technique that converts malware samples into grayscale images and then scans the image for textural and structural patterns specific to malware samples.
HOW STAMINA ACTUALLY WORKS
[...] Researchers used 60% of the known malware samples to train the original DNN algorithm, 20% of the files to validate the DNN, and the other 20% for the actual testing process.
The research team said STAMINA achieved an accuracy of 99.07% in identifying and classifying malware samples, with a false positives rate of 2.58%.

"The results certainly encourage the use of deep transfer learning for the purpose of malware classification," said Jugal Parikh and Marc Marino, the two Microsoft researchers who participated in the research on behalf of the Microsoft Threat Protection Intelligence Team.
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Microsoft said that while STAMINA was accurate and fast when working with smaller files, it faultered with larger ones.

Currently, Microsoft can make this approach work better than other companies primarily because of the sheer data it possesses from the hundreds of millions of Windows Defender installs.

"Anybody can build a model, but the labeled data and the quantity of it and the quality of it, really helps train the machine learning models appropriately and hence defines how effective they are going to be," Ganacharya said.
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Microsoft: Microsoft researchers work with Intel Labs to explore new deep learning approaches for malware classification

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