  Preventing organizations from Cyber exploits needs timely intelligence about
Cyber vulnerabilities and attacks, referred as threats. Cyber threat
intelligence can be extracted from various sources including social media
platforms where users publish the threat information in real time. Gathering
Cyber threat intelligence from social media sites is a time consuming task for
security analysts that can delay timely response to emerging Cyber threats. We
propose a framework for automatically gathering Cyber threat intelligence from
Twitter by using a novelty detection model. Our model learns the features of
Cyber threat intelligence from the threat descriptions published in public
repositories such as Common Vulnerabilities and Exposures (CVE) and classifies
a new unseen tweet as either normal or anomalous to Cyber threat intelligence.
We evaluate our framework using a purpose-built data set of tweets from 50
influential Cyber security related accounts over twelve months (in 2018). Our
classifier achieves the F1-score of 0.643 for classifying Cyber threat tweets
and outperforms several baselines including binary classification models. Our
analysis of the classification results suggests that Cyber threat relevant
tweets on Twitter do not often include the CVE identifier of the related
threats. Hence, it would be valuable to collect these tweets and associate them
with the related CVE identifier for cyber security applications.
