TY - GEN
T1 - Smart Vulnerability Assessment for Scientific Cyberinfrastructure
T2 - 18th IEEE International Conference on Intelligence and Security Informatics, ISI 2020
AU - Ullman, Steven
AU - Samtani, Sagar
AU - Lazarine, Ben
AU - Zhu, Hongyi
AU - Ampel, Benjamin
AU - Patton, Mark
AU - Chen, Hsinchun
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/11/9
Y1 - 2020/11/9
N2 - The accelerated growth of computing technologies has provided interdisciplinary teams a platform for producing innovative research at an unprecedented speed. Advanced scientific cyberinfrastructures, in particular, provide data storage, applications, software, and other resources to facilitate the development of critical scientific discoveries. Users of these environments often rely on custom developed virtual machine (VM) images that are comprised of a diverse array of open source applications. These can include vulnerabilities undetectable by conventional vulnerability scanners. This research aims to identify the installed applications, their vulnerabilities, and how they vary across images in scientific cyberinfrastructure. We propose a novel unsupervised graph embedding framework that captures relationships between applications, as well as vulnerabilities identified on corresponding GitHub repositories. This embedding is used to cluster images with similar applications and vulnerabilities. We evaluate cluster quality using Silhouette, Calinski-Harabasz, and Davies-Bouldin indices, and application vulnerabilities through inspection of selected clusters. Results reveal that images pertaining to genomics research in our research testbed are at greater risk of high-severity shell spawning and data validation vulnerabilities.
AB - The accelerated growth of computing technologies has provided interdisciplinary teams a platform for producing innovative research at an unprecedented speed. Advanced scientific cyberinfrastructures, in particular, provide data storage, applications, software, and other resources to facilitate the development of critical scientific discoveries. Users of these environments often rely on custom developed virtual machine (VM) images that are comprised of a diverse array of open source applications. These can include vulnerabilities undetectable by conventional vulnerability scanners. This research aims to identify the installed applications, their vulnerabilities, and how they vary across images in scientific cyberinfrastructure. We propose a novel unsupervised graph embedding framework that captures relationships between applications, as well as vulnerabilities identified on corresponding GitHub repositories. This embedding is used to cluster images with similar applications and vulnerabilities. We evaluate cluster quality using Silhouette, Calinski-Harabasz, and Davies-Bouldin indices, and application vulnerabilities through inspection of selected clusters. Results reveal that images pertaining to genomics research in our research testbed are at greater risk of high-severity shell spawning and data validation vulnerabilities.
KW - GitHub
KW - Graph Embedding
KW - Scientific cyberinfrastructure
KW - virtual machine
KW - vulnerability scanning
UR - http://www.scopus.com/inward/record.url?scp=85098991140&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85098991140&partnerID=8YFLogxK
U2 - 10.1109/ISI49825.2020.9280545
DO - 10.1109/ISI49825.2020.9280545
M3 - Conference contribution
AN - SCOPUS:85098991140
T3 - Proceedings - 2020 IEEE International Conference on Intelligence and Security Informatics, ISI 2020
BT - Proceedings - 2020 IEEE International Conference on Intelligence and Security Informatics, ISI 2020
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 9 November 2020 through 10 November 2020
ER -