Machine Learning Can Use Tweets To Spot Critical Security Flaws

Researchers at Ohio State University, the security company FireEye, and research firm Leidos last week published a paper [PDF] describing a new system that reads millions of tweets for mentions of software security vulnerabilities, and then, using their machine-learning-trained algorithm, assessed how much of a threat they represent based on how they're described. From a report: They found that Twitter can not only predict the majority of security flaws that will show up days later on the National Vulnerability Database -- the official register of security vulnerabilities tracked by the National Institute of Standards and Technology -- but that they could also use natural language processing to roughly predict which of those vulnerabilities will be given a 'high' or 'critical' severity rating with better than 80 percent accuracy.

'We think of it almost like Twitter trending topics,' says Alan Ritter, an Ohio State professor who worked on the research and will be presenting it at the North American Chapter of the Association for Computational Linguistics in June. 'These are trending vulnerabilities.' A work-in-progress prototype they've put online, for instance, surfaces tweets from the last week about a fresh vulnerability in MacOS known as 'BuggyCow,' as well as an attack known as SPOILER that could allow webpages to exploit deep-seated vulnerabilities in Intel chips. Neither of the attacks, which the researchers' Twitter scanner labeled 'probably severe,' has shown up yet in the National Vulnerability Database.

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