Improving Attribution & Malware Identification With Machine Learning

New technique may be able to predict not only whether unfamiliar, unknown code is malicious, but also what family it is and who it came from. One of the cybersecurity promises of machine learning (particularly “deep learning”) is that it can accurately identify malware nobody has ever seen before because of what it’s learned about malware it’s seen in the past. Konstantin Berlin, senior research engineer at Invincea Labs, is trying to take the techology further, so that organizations can get more information about unfamiliar code than simply “it’s benign” or “it’s malicious.” Berlin, who will be presenting his work next month at Black Hat, says security pros also want to know more about the malware family so they can plan their mitigation strategy accordingly. His technique, he says will…


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