2026-09-18T17:25:42Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1629802026-01-16T20:08:01Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Pal, Ranjan
Siegel, Michael D.
Yao, Darren Z.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2025-10-06T17:37:57Z
2025-10-06T17:37:57Z
2025-05
2025-06-23T14:04:37.146Z
https://hdl.handle.net/1721.1/162980
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.
M.Eng.
Massachusetts Institute of Technology
In Copyright - Educational Use Permitted
Copyright retained by author(s)
https://rightsstatements.org/page/InC-EDU/1.0/
Graph Metrics for Improving Cybersecurity on Software Dependency Networks
Thesis
application/pdf
Master
Master of Engineering in Electrical Engineering and Computer Science
Publication
unknown
unknown
<Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="215d523a-7111-4594-8caf-3baf3b521d12">
<Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type>
<Title>Graph Metrics for Improving Cybersecurity on Software Dependency Networks</Title>
<PublishedIn>
<Publication>
</Publication>
</PublishedIn>
<PublicationDate>2025-05</PublicationDate>
<Authors>
<Author>
<DisplayName>Yao, Darren Z.</DisplayName>
<Affiliation>
<OrgUnit>
</OrgUnit>
</Affiliation>
</Author>
</Authors>
<Editors>
</Editors>
<Publishers>
<Publisher>
<DisplayName>Massachusetts Institute of Technology</DisplayName>
<OrgUnit />
</Publisher>
</Publishers>
<License>https://rightsstatements.org/page/InC-EDU/1.0/</License>
<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.</Abstract>
<Access xmlns="http://purl.org/coar/access_right"
>
</Access>
</Publication>