Identifying and Categorizing Malicious Content on Paste Sites: A Neural Topic Modeling Approach

Tala Vahedi, Benjamin M. Ampel, Sagar Samtani, Hsinchun Chen
Last updated on Jun 9, 2026
PDF DOI
In Plain Terms

Cybercriminals dump stolen data, credit card numbers, and malware code onto public text-sharing sites like Pastebin. This study builds a new machine-learning method that combines a language model (BERT) with topic modeling to automatically sort millions of these posts into categories, helping security teams spot leaked sensitive information and emerging threats faster.

Artifacts

PDF DOI

Related Papers

Cite this

APA Vahedi, T., Ampel, B. M., Samtani, S., & Chen, H. (2021). Identifying and Categorizing Malicious Content on Paste Sites: A Neural Topic Modeling Approach. In 2021 IEEE International Conference on Intelligence and Security Informatics (ISI). https://doi.org/10.1109/ISI53945.2021.9624765
BibTeX
@inproceedings{vahedi2021identifying,
  title={Identifying and Categorizing Malicious Content on Paste Sites: A Neural Topic Modeling Approach},
  author={Tala Vahedi and Benjamin M. Ampel and Sagar Samtani and Hsinchun Chen},
  booktitle={2021 IEEE International Conference on Intelligence and Security Informatics (ISI)},
  year={2021},
  doi={10.1109/ISI53945.2021.9624765}
}
Hacker Forums Cyber Threat Intelligence Deep Learning Transformers / LLMs 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.