  Many cyber network defense tools rely on the National Vulnerability Database
(NVD) to provide timely information on known vulnerabilities that exist within
systems on a given network. However, recent studies have indicated that the NVD
is not always up to date, with known vulnerabilities being discussed publicly
on social media platforms, like Twitter and Reddit, months before they are
published to the NVD. To that end, we present a framework for unsupervised
classification to filter tweets for relevance to cyber security. We consider
and evaluate two unsupervised machine learning techniques for inclusion in our
framework, and show that zero-shot classification using a Bidirectional and
Auto-Regressive Transformers (BART) model outperforms the other technique with
83.52% accuracy and a F1 score of 83.88, allowing for accurate filtering of
tweets without human intervention or labelled data for training. Additionally,
we discuss different insights that can be derived from these cyber-relevant
tweets, such as trending topics of tweets and the counts of Twitter mentions
for Common Vulnerabilities and Exposures (CVEs), that can be used in an alert
or report to augment current NVD-based risk assessment tools.
