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Turns out converting files into images is a highly effective way to detect malware


A branch of artificial intelligence called machine learning is all around us. It’s employed by Facebook to help curate content (and target us with ads), Google uses it to filter millions of spam messages each day, and it’s part of what enabled the OpenAI bot to beat the reigning Dota 2 champions last year in two out of three matches. There are seemingly endless uses. Adding one more to the pile, Microsoft and Intel have come up with a clever machine learning framework that is surprisingly accurate at detecting malware through a grayscale image conversion process.

Microsoft detailed the technology in a blog post (via ZDNet), which it calls static malware-as-image network analysis, or STAMINA. It consists of a three-step process. In simple terms, the machine learning project starts out by taking binary files and converting them into two-dimensional images.

(Image credit: Microsoft)

The images are then fed into the framework. This second step is a process called transfer learning, which essentially helps the algorithm build upon its existing knowledge, while comparing images against its existing training.

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