# Social Media Monitoring for IoT Cyber-Threats

arXiv: https://arxiv.org/abs/2109.04306 · HTML: https://mlsanity.com/paper/2109.04306/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 32 references that are in the map.
- First posted on arXiv 9 Sep 2021.

## Builds on (most cited first, 5 of 5)

- [Automated software vulnerability detection with machine learning](https://mlsanity.com/paper/1803.04497/) (2018)
- [Processing Tweets for Cybersecurity Threat Awareness](https://mlsanity.com/paper/1904.02072/) (2019)
- [Early Warnings of Cyber Threats in Online Discussions](https://mlsanity.com/paper/1801.09781/) (2018)
- [CVE based classification of vulnerable IoT systems](https://mlsanity.com/paper/2006.16640/) (2020)
- [Gathering Cyber Threat Intelligence from Twitter Using Novelty Classification](https://mlsanity.com/paper/1907.01755/) (2019)

## Built on by (most cited first, 1 of 1)

- [What are the attackers doing now? Automating cyber threat intelligence extraction from text on pace with the changing threat landscape: A survey](https://mlsanity.com/paper/2109.06808/) (2021)

## Abstract (arXiv)

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.