Machine Learning-based Vulnerability Study of Interpose PUFs as Security Primitives for IoT Networks

2021 IEEE International Conference on Networking, Architecture and Storage (NAS)(2021)

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摘要
Security is of importance for communication networks, and many network nodes, like sensors and IoT devices, are resource-constrained. Physical Unclonable Functions (PUFs) leverage physical variations of the integrated circuits to produce responses unique to individual circuits and have the potential for delivering security for low-cost networks. But before a PUF can be adopted for security applica...
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关键词
Training,Neural networks,Machine learning,Network security,Physical unclonable function,Nonhomogeneous media,Reliability engineering
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