  The rapid development of IoT applications and their use in various fields of
everyday life has resulted in an escalated number of different possible
cyber-threats, and has consequently raised the need of securing IoT devices.
Collecting Cyber-Threat Intelligence (e.g., zero-day vulnerabilities or
trending exploits) from various online sources and utilizing it to proactively
secure IoT systems or prepare mitigation scenarios has proven to be a promising
direction. In this work, we focus on social media monitoring and investigate
real-time Cyber-Threat Intelligence detection from the Twitter stream.
Initially, we compare and extensively evaluate six different machine-learning
based classification alternatives trained with vulnerability descriptions and
tested with real-world data from the Twitter stream to identify the
best-fitting solution. Subsequently, based on our findings, we propose a novel
social media monitoring system tailored to the IoT domain; the system allows
users to identify recent/trending vulnerabilities and exploits on IoT devices.
Finally, to aid research on the field and support the reproducibility of our
results we publicly release all annotated datasets created during this process.
