Graph Metrics for Improving Cybersecurity on Software Dependency Networks
Name
yao-dyao-meng-eecs-2025-thesis.pdf
Description
Thesis PDF
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488.6 KB
Format
Adobe PDF
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0cb3332924e5d740b684e78cb39cb081
Author(s)
Yao, Darren Z.
Advisor(s)
Pal, Ranjan
Siegel, Michael D.
Date Issued
May 2025
Publisher
Massachusetts Institute of Technology
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.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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