Yggdrasil — Early Detection of Cybernetic Vulnerabilities from Twitter

The use of computational systems for enhancing collaborative learning is widely spread. While interest in the application of Computer-Supported Collaborative Learning (CSCL) systems in cybersecurity exists, endeavors in this sense are difficult to compare due to different choices of methods and tools. In this sense, we advance a typology of CSCL systems in cybersecurity and introduce an automated system, Yggdrasil, which identifies emerging vulnerability threats from tweets containing references to explicit news articles. Such a system can be used to enhance collaborative learning processes of experts and non-experts in cybersecurity regarding the early detection of vulnerabilities, by automatically extracting information from online communities. For this purpose, we built a corpus of 650 annotated tweets that contain links to cybersecurity related articles, posted by key profiles in the cybersecurity landscape. Using insights from state-of-the-art approaches in early detection of vulnerabilities from tweets and sentiment analysis from Twitter, we conducted two experiments for the development of Yggdrasil. Our results argue that the use of the BERT language model is an adequate approach for identifying relevant posts or news articles, having an accuracy over 91% in various configurations. Furthermore, we also illustrate the use of transfer learning as a viable alternative for the early detection of cybernetic vulnerabilities.