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首页> 外文会议>Conference on the North American Chapter of the Association for Computational Linguistics: Human Language Technologies >Analyzing the Perceived Severity of Cybersecurity Threats Reported on Social Media
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Analyzing the Perceived Severity of Cybersecurity Threats Reported on Social Media

机译：分析社交媒体上报告的网络安全威胁的感知严重程度

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摘要

Breaking cybersecurity events are shared across a range of websites, including security blogs (FireEye, Kaspersky, etc.). in addition to social media platforms such as Face-book and Twitter. In this paper, we investigate methods to analyze the severity of cybersecurity threats based on the language that is used to describe them online. A corpus of 6,000 tweets describing software vulnerabilities is annotated with authors' opinions toward their severity. We show that our corpus supports the development of automatic classifiers with high precision for this task. Furthermore, we demonstrate the value of analyzing users' opinions about the severity of threats reported online as an early indicator of important software vulnerabilities. We present a simple, yet effective method for linking software vulnerabilities reported in tweets to Common Vulnerabilities and Exposures (CVEs) in the National Vulnerability Database (NVD). Using our predicted severity scores, we show that it is possible to achieve a Precision?50 of 0.86 when forecasting high severity vulnerabilities, significantly outperforming a baseline that is based on tweet volume. Finally we show how reports of severe vulnerabilities online are predictive of real-world exploits.
机译：打破网络安全事件在一系列网站上共享，包括安全博客（Fireeye，Kaspersky等）。除了社交媒体平台，如面书和推特。在本文中，我们调查了根据用于在线描述它们的语言来分析分析网络安全威胁的严重程度的方法。描述软件漏洞的6,000名推文的语料库是通过作者对其严重性的看法进行了诠释。我们表明我们的语料库支持高精度的自动分类器的开发。此外，我们展示了分析用户意见关于在线报告的威胁严重程度作为重要软件漏洞的早期指标的价值。我们为在国家漏洞数据库（NVD）中的常见漏洞和曝光（CVE）中提出了一种简单但有效的方法，用于链接推文中报告的软件漏洞。使用我们预测的严重性分数，我们表明，在预测高度严重性漏洞时，可以实现精度？50的0.86，显着优于基于推文卷的基线。最后，我们展示了如何在线的严重漏洞的报告是如何预测现实世界的漏洞利用。

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