| dc.contributor.advisor | Pal, Ranjan | |
| dc.contributor.advisor | Siegel, Michael D. | |
| dc.contributor.author | Yao, Darren Z. | |
| dc.date.accessioned | 2025-10-06T17:37:57Z | |
| dc.date.available | 2025-10-06T17:37:57Z | |
| dc.date.issued | 2025-05 | |
| dc.date.submitted | 2025-06-23T14:04:37.146Z | |
| dc.identifier.uri | https://hdl.handle.net/1721.1/162980 | |
| dc.description.abstract | Modern software ecosystems are deeply interconnected, allowing a vulnerability in a single component to propagate and affect many others. In this thesis, we model software ecosystems as directed graphs, and apply various graph-theoretic metrics to quantify security risk. We compare two deep learning frameworks (PyTorch and TensorFlow) with two traditional software frameworks (npm and PyPI), identifying critical properties of their dependency structures, which motivates several recommendations for improving software supply chain security. | |
| dc.publisher | Massachusetts Institute of Technology | |
| dc.rights | In Copyright - Educational Use Permitted | |
| dc.rights | Copyright retained by author(s) | |
| dc.rights.uri | https://rightsstatements.org/page/InC-EDU/1.0/ | |
| dc.title | Graph Metrics for Improving Cybersecurity on Software Dependency Networks | |
| dc.type | Thesis | |
| dc.description.degree | M.Eng. | |
| dc.contributor.department | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science | |
| mit.thesis.degree | Master | |
| thesis.degree.name | Master of Engineering in Electrical Engineering and Computer Science | |
