Labeling Hacker Exploits for Proactive Cyber Threat Intelligence: A Deep Transfer Learning Approach

Benjamin M. Ampel, Sagar Samtani, Hongyi Zhu, Steven Ullman, Hsinchun Chen
Last updated on Jun 9, 2026
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This study builds a system that automatically collects and categorizes exploit code shared on hacker forums, turning messy, unlabeled posts into useful early-warning cyber threat intelligence. It uses transfer learning to apply knowledge from professionally labeled exploits to noisy forum data, sorting exploits into eight categories. The proposed model outperforms existing approaches in the literature.

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APA Ampel, B. M., Samtani, S., Zhu, H., Ullman, S., & Chen, H. (2020). Labeling Hacker Exploits for Proactive Cyber Threat Intelligence: A Deep Transfer Learning Approach. In 2020 IEEE International Conference on Intelligence and Security Informatics (ISI). https://doi.org/10.1109/ISI49825.2020.9280548
BibTeX
@inproceedings{ampel2020labeling,
  title={Labeling Hacker Exploits for Proactive Cyber Threat Intelligence: A Deep Transfer Learning Approach},
  author={Benjamin M. Ampel and Sagar Samtani and Hongyi Zhu and Steven Ullman and Hsinchun Chen},
  booktitle={2020 IEEE International Conference on Intelligence and Security Informatics (ISI)},
  year={2020},
  doi={10.1109/ISI49825.2020.9280548}
}
Hacker Forums Cyber Threat Intelligence Deep Learning Exploit Labeling
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.