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Breaking cybersecurity events are shared across a range of websites,including security blogs (FireEye, Kaspersky, etc.), in addition to socialmedia platforms such as Facebook and Twitter. In this paper, we investigatemethods to analyze the severity of cybersecurity threats based on the languagethat is used to describe them online. A corpus of 6,000 tweets describingsoftware vulnerabilities is annotated with authors' opinions toward theirseverity. We show that our corpus supports the development of automaticclassifiers with high precision for this task. Furthermore, we demonstrate thevalue of analyzing users' opinions about the severity of threats reportedonline as an early indicator of important software vulnerabilities. We presenta simple, yet effective method for linking software vulnerabilities reported intweets to Common Vulnerabilities and Exposures (CVEs) in the NationalVulnerability Database (NVD). Using our predicted severity scores, we show thatit is possible to achieve a Precision@50 of 0.86 when forecasting high severityvulnerabilities, significantly outperforming a baseline that is based on tweetvolume. Finally we show how reports of severe vulnerabilities online arepredictive of real-world exploits.

Analyzing the Perceived Severity of Cybersecurity Threats Reported on Social Media