Physical Unclonable Functions
Donated on 10/7/2018
The dataset is generated from Physical Unclonable Functions (PUFs) simulation, specifically XOR Arbiter PUFs. PUFs are used for authentication purposes. For more info, refer to our paper below.
Dataset Characteristics
Multivariate
Subject Area
Computer Science
Associated Tasks
Classification
Feature Type
Integer
# Instances
6000000
# Features
-
Dataset Information
Has Missing Values?
No
Variables Table
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Additional Variable Information
There are two datasets generated from k-XOR Arbiter PUFs simulation: (1) 5-XOR_128bit dataset: This dataset is generated using 5-XOR arbiters of 128bit stages PUF. It consists of 6 million rows and 129 attributes where the last attribute is the class label (1 or -1). It is divided into two sets: training set (5 million) and testing set (1 million). (1) 6-XOR_64bit dataset: This dataset is generated using 6-XOR arbiters of 64bit stages PUF. It consists of 2.4 million rows and 65 attributes where the last attribute is the class label (1 or -1). It is divided into two sets: training set (2 million) and testing set (400K).
Dataset Files
File | Size |
---|---|
XOR_Arbiter_PUFs/5xor_128bit/train_5xor_128dim.csv | 1.5 GB |
XOR_Arbiter_PUFs/6xor_64bit/train_6xor_64dim.csv | 309.9 MB |
XOR_Arbiter_PUFs/5xor_128bit/test_5xor_128dim.csv | 307.6 MB |
XOR_Arbiter_PUFs/6xor_64bit/test_6xor_64dim.csv | 62 MB |
XOR_Arbiter_PUFs/.DS_Store | 6 KB |
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pip install ucimlrepo
from ucimlrepo import fetch_ucirepo # fetch dataset physical_unclonable_functions = fetch_ucirepo(id=463) # data (as pandas dataframes) X = physical_unclonable_functions.data.features y = physical_unclonable_functions.data.targets # metadata print(physical_unclonable_functions.metadata) # variable information print(physical_unclonable_functions.variables)
Aseeri, A., Alkatheiri, M., & Zhuang, Y. (2018). Physical Unclonable Functions [Dataset]. UCI Machine Learning Repository. https://doi.org/10.24432/C5D03R.
Creators
Ahmad Aseeri
Mohammed Alkatheiri
Yu Zhuang
DOI
License
This dataset is licensed under a Creative Commons Attribution 4.0 International (CC BY 4.0) license.
This allows for the sharing and adaptation of the datasets for any purpose, provided that the appropriate credit is given.