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PolyPUF: Physically Secure Self-Divergence
Sven Tenzing Choden Konigsmark
, Deming Chen
,
Martin D. F. Wong
Office of the Provost
Research output
:
Contribution to journal
›
Journal article
›
peer-review
20
Citations (Scopus)
Overview
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Dive into the research topics of 'PolyPUF: Physically Secure Self-Divergence'. Together they form a unique fingerprint.
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Computer Science
Machine Learning
100%
Learning System
100%
Authentication
50%
Neural Network
50%
Reproducibility
50%
Machine Learning Algorithm
50%
Response Function
50%
Threat Analysis
50%
Expected Response
50%
Reference Architecture
50%
Security Function
50%
Keyphrases
Divergence
100%
Physically Unclonable Function
100%
Machine Learning Attack
22%
Encryption
11%
Adversary
11%
Resistivity
11%
Effective Techniques
11%
Machine Learning Algorithms
11%
Model Building
11%
Software-based
11%
Function Response
11%
Complex Neural Network
11%
Advanced Machine Learning
11%
Accurately Model
11%
Scalable Architecture
11%
Expected Response
11%
Reference Architecture
11%
Secure Architecture
11%
Function Configuration
11%
Challenge-response
11%
Response Map
11%
Network Machine Learning
11%