From Word Embedding to Cyber-Phrase Embedding: Comparison of Processing Cybersecurity Texts

Much of the vital information about emerging threats and the corresponding defensive measures are contained in large volumes of natural language texts online. Capturing such actionable intelligence in real-time is critical to prevent large scale attacks automatically. The ATTCK framework is a widely recognized standard to catalog technical details of cyber threats and deploy mitigating measures. A technique in ATTCK specifies a set of adversary actions to achieve a particular goal, such as Exfiltration over Command and Control channel. Details of the technique include encrypted traffic and encoded data. A key challenge in identifying such cyber intelligence from natural language texts is that for a given action, such as encrypted traffic, many alternative expressions are possible (e.g., send using a self-signed certificate, send using HTTPS requests). It is not practical to manually provide an exhaustive list of all such variants. We demonstrate that using cyber-phrase embedding on a cybersecurity text corpus is a promising approach to overcome such difficulties. Our evaluation demonstrates that our model outperforms existing models. We have created an open-source project to make our tools and data available for the cybersecurity research community.