Exploring the Evolution of Exploit-Sharing Hackers: An Unsupervised Graph Embedding Approach

Kaeli Otto, Benjamin M. Ampel, Sagar Samtani, Hongyi Zhu, Hsinchun Chen
Last updated on Jun 9, 2026
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This research develops a framework to track how influential hackers in dark-web forums change and grow over time, rather than just snapshotting them at one moment. It combines each hacker’s activity statistics, network position, and writing style using graph-based machine learning, with a technique to compare these profiles across multiple time periods. In a case study of an English forum with over 51,000 posts, the method successfully pinpointed key hackers distributing pirated material.

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APA Otto, K., Ampel, B. M., Samtani, S., Zhu, H., & Chen, H. (2021). Exploring the Evolution of Exploit-Sharing Hackers: An Unsupervised Graph Embedding Approach. In 2021 IEEE International Conference on Intelligence and Security Informatics (ISI). https://doi.org/10.1109/ISI53945.2021.9624846
BibTeX
@inproceedings{otto2021exploring,
  title={Exploring the Evolution of Exploit-Sharing Hackers:  An Unsupervised Graph Embedding Approach},
  author={Kaeli Otto and Benjamin M. Ampel and Sagar Samtani and Hongyi Zhu and Hsinchun Chen},
  booktitle={2021 IEEE International Conference on Intelligence and Security Informatics (ISI)},
  year={2021},
  doi={10.1109/ISI53945.2021.9624846}
}
Hacker Forums Cyber Threat Intelligence Graph Embedding Deep Learning
Benjamin M. Ampel

Benjamin M. Ampel

Assistant Professor in Computer Information Systems and Director, Center for CyberAI Research (CCAIR)

My research focuses on AI-enabled Cybersecurity, including Cyber Threat Intelligence, Large Language Models, and Phishing Detection.