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dc.contributor.authorVasilakis, Nikos
dc.contributor.authorBenetopoulos, Achilles
dc.contributor.authorHanda, Shivam
dc.contributor.authorSchoen, Alizee
dc.contributor.authorShen, Jiasi
dc.contributor.authorRinard, Martin
dc.date.accessioned2022-11-10T14:39:16Z
dc.date.available2022-11-10T14:39:16Z
dc.date.issued2021-11-12
dc.identifier.isbn978-1-4503-8454-4
dc.identifier.urihttps://hdl.handle.net/1721.1/146310
dc.publisherACM|Proceedings of the 2021 ACM SIGSAC Conference on Computer and Communications Securityen_US
dc.relation.isversionofhttps://doi.org/10.1145/3460120.3484736en_US
dc.rightsArticle is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.en_US
dc.sourceACM|Proceedings of the 2021 ACM SIGSAC Conference on Computer and Communications Securityen_US
dc.titleSupply-Chain Vulnerability Elimination via Active Learning and Regenerationen_US
dc.typeArticleen_US
dc.identifier.citationVasilakis, Nikos, Benetopoulos, Achilles, Handa, Shivam, Schoen, Alizee, Shen, Jiasi et al. 2021. "Supply-Chain Vulnerability Elimination via Active Learning and Regeneration."
dc.contributor.departmentMassachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
dc.identifier.mitlicensePUBLISHER_POLICY
dc.eprint.versionFinal published versionen_US
dc.type.urihttp://purl.org/eprint/type/ConferencePaperen_US
eprint.statushttp://purl.org/eprint/status/NonPeerRevieweden_US
dc.date.updated2022-11-02T22:16:38Z
dc.language.rfc3066en
dc.rights.holderThe author(s)
dspace.date.submission2022-11-02T22:16:39Z
mit.licensePUBLISHER_POLICY
mit.metadata.statusAuthority Work and Publication Information Neededen_US


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