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