Smart Vulnerability Assessment for Scientific Cyberinfrastructure: An Unsupervised Graph Embedding Approach

Steven Ullman, Sagar Samtani, Ben Lazarine, Hongyi Zhu, Benjamin M. Ampel, Mark Patton, Hsinchun Chen
Last updated on Jun 9, 2026
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In Plain Terms

Scientific computing platforms rely on custom virtual machine images packed with open-source software that can hide security flaws ordinary scanners miss. This study introduces a machine-learning method that groups these VM images by their applications and vulnerabilities, revealing that genomics-research images were especially exposed to high-severity flaws.

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APA Ullman, S., Samtani, S., Lazarine, B., Zhu, H., Ampel, B. M., Patton, M., & Chen, H. (2020). Smart Vulnerability Assessment for Scientific Cyberinfrastructure: An Unsupervised Graph Embedding Approach. In 2020 IEEE International Conference on Intelligence and Security Informatics (ISI). https://doi.org/10.1109/ISI49825.2020.9280545
BibTeX
@inproceedings{ullman2020smart,
  title={Smart Vulnerability Assessment for Scientific Cyberinfrastructure: An Unsupervised Graph Embedding Approach},
  author={Steven Ullman and Sagar Samtani and Ben Lazarine and Hongyi Zhu and Benjamin M. Ampel and Mark Patton and Hsinchun Chen},
  booktitle={2020 IEEE International Conference on Intelligence and Security Informatics (ISI)},
  year={2020},
  doi={10.1109/ISI49825.2020.9280545}
}
Vulnerability Scanning Deep Learning Graph Embedding GitHub
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.