On the public citation graph

Security Analysis of Digital-Based Physically Unclonable Functions: Dataset Generation, Machine Learning Modeling, and Correlation Analysis

0 signed · 16 inferred · view as graph

Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.

Is this your work? Sign in with ORCID and put your name on the claims that are yours.
Claim with ORCID
Reference health. See whether every reference in this work resolves to a known work and none has been retracted — and get a dated CiteStamped badge you can embed.
See reference health

What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/s10207-020-00513-8openalex-extracted
Anti-BlUFf: towards counterfeit mitigation in IC supply chains using blockchain and PUF
inferredcites →https://doi.org/10.1109/access.2021.3066948openalex-extracted
Hybrid Boolean Networks as Physically Unclonable Functions
inferredcites →https://doi.org/10.1109/access.2022.3151966openalex-extracted
Compact PUF Design With Systematic Biases Mitigation on Xilinx FPGAs
inferredcites →https://doi.org/10.1109/ecctd49232.2020.9218325openalex-extracted
Spatial Context Tree Weighting for Physical Unclonable Functions
inferredcites →https://doi.org/10.1109/lssc.2022.3158630openalex-extracted
A 3T eDRAM In-Memory Physically Unclonable Function With Spatial Majority Voting Stabilization
inferredcites →https://doi.org/10.1109/tcad.2021.3129112openalex-extracted
Correlation Integral-Based Intrinsic Dimension: A Deep-Learning-Assisted Empirical Metric to Estimate the Robustness of Physically Unclonable Functions to Modeling Attacks
inferredcites →https://doi.org/10.1109/tdsc.2018.2822298openalex-extracted
Decay-Based DRAM PUFs in Commodity Devices
inferredcites →https://doi.org/10.1109/tifs.2013.2279798openalex-extracted
PUF Modeling Attacks on Simulated and Silicon Data
inferredcites →https://doi.org/10.1109/tifs.2018.2791341openalex-extracted
Spatial Correlation Analysis on Physical Unclonable Functions
inferredcites →https://doi.org/10.1109/tifs.2022.3189533openalex-extracted
Neural Network Modeling Attacks on Arbiter-PUF-Based Designs
inferredcites →https://doi.org/10.1145/3445978openalex-extracted
Machine Learning Vulnerability Analysis of FPGA-based Ring Oscillator PUFs and Counter Measures
inferredcites →https://doi.org/10.3390/electronics9101715openalex-extracted
A Fast Deep Learning Method for Security Vulnerability Study of XOR PUFs
inferredcites →https://doi.org/10.46586/tches.v2021.i3.520-551openalex-extracted
Inconsistency of Simulation and Practice in Delay-based Strong PUFs
inferredcites →https://doi.org/10.48550/arxiv.2005.04344openalex-extracted
Physical Security in the Post-quantum Era: A Survey on Side-channel Analysis, Random Number Generators, and Physically Unclonable Functions

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.1109/gcwkshp64532.2024.11101578openalex-snapshot
LPUF-AuthNet: A Lightweight PUF-Based IoT Authentication via Tandem Neural Networks and Split Learning
inferred← citeshttps://doi.org/10.3390/fi17070275openalex-snapshot
Integrating Physical Unclonable Functions with Machine Learning for the Authentication of Edge Devices in IoT Networks
Editorial review for independent work. ICSAC — the Institute for Complexity Science and Advanced Computing — runs a structured editorial panel for researchers publishing outside journals, and publishes on CiteStamp rails. Submission is free and goes through ORCID: icsacinstitute.org/submit.