{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/162980"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/162980","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"Graph Metrics for Improving Cybersecurity on Software Dependency Networks","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_html":"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_has_math":false,"creators":["Yao, Darren Z."],"institution":"Massachusetts Institute of Technology","degree_name":"Master","degree_level":null,"degree_discipline":null,"degree_department":"Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science","school":null,"contributors":[],"advisors":["Pal, Ranjan","Siegel, Michael D."],"committee_chairs":[],"committee_members":[],"year":2025,"date_issued":"2025-05","date_published":"2025-05","updated_at":"2026-07-22T22:20:59Z","subjects":[],"languages":[],"rights":["In Copyright - Educational Use Permitted","Copyright retained by author(s)"],"rights_urls":["https://rightsstatements.org/page/InC-EDU/1.0/"],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/1721.1/162980","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Pal, Ranjan","Siegel, Michael D."]},{"key":"dc:contributor.department","label":"Department","values":["Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science"]},{"key":"dc:creator","label":"Author","values":["Yao, Darren Z."]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2025-10-06T17:37:57Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2025-10-06T17:37:57Z"]},{"key":"dc:date.issued","label":"Date","values":["2025-05"]},{"key":"dc:publisher","label":"Institution","values":["Massachusetts Institute of Technology"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master","Master of Engineering in Electrical Engineering and Computer Science"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:rights","label":"Dc Rights","values":["In Copyright - Educational Use Permitted","Copyright retained by author(s)"]},{"key":"dc:rights.uri","label":"Rights URI","values":["https://rightsstatements.org/page/InC-EDU/1.0/"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/1721.1/162980"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["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."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["M.Eng."]},{"key":"dc:title","label":"Title","values":["Graph Metrics for Improving Cybersecurity on Software Dependency Networks"]}]}],"canonical_facts":{"dc:contributor.advisor":["Pal, Ranjan","Siegel, Michael D."],"dc:contributor.department":["Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science"],"dc:creator":["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: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:description.degree":["M.Eng."],"dc:identifier.uri":["https://hdl.handle.net/1721.1/162980"],"dc:publisher":["Massachusetts Institute of Technology"],"dc:rights":["In Copyright - Educational Use Permitted","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"],"thesis:degree_name":["Master","Master of Engineering in Electrical Engineering and Computer Science"]},"updated_at":"2026-07-22T22:20:59Z"}