SECURING MACHINE LEARNING SUPPLY CHAINS
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Authors
Strubel, Joshua D.
Subjects
machine learning
software development life cycle
cyber security
model integrity
software development life cycle
cyber security
model integrity
Advisors
Kroll, Joshua A.
Orescanin, Marko
Date of Issue
2021-09
Date
Publisher
Monterey, CA; Naval Postgraduate School
Language
Abstract
Recent cyber-attacks on supply chains such as the large-scale SolarWinds attack are gaining the attention of cybersecurity experts. Supply chain attacks are growing in frequency and are taking advantage of the trust that organizations put in the dependencies of their supply. The machine learning supply chain is incredibly vulnerable to this category of attack because of the large number of dependencies utilized. We demonstrate a weakness in a machine learning supply chain by attacking the model's parameters. We then demonstrate how an organization can implement secure checkpoints that generate integrity metadata and detect this class of attack before proceeding to the next phase in the supply chain.
Type
Thesis
Description
Series/Report No
Department
Computer Science (CS)
Organization
Identifiers
NPS Report Number
Sponsors
Funder
Format
Citation
Distribution Statement
Approved for public release. Distribution is unlimited.
Rights
This publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.