Source:

Ahmad O. Aseeri (a.aseeri '@' psau.edu.sa), Yu Zhuang (yu.zhuang '@' ttu.edu) - Department of Computer Science, Texas Tech University, United States
Mohammed Saeed Alkatheiri (msalkatheri '@' uj.edu.sa) - Faculty of Computing and Information Technology, University of Jeddah, Saudi Arabia

Data Set Information:

Attribute 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).

Relevant Papers:

[1] Aseeri, A. O., Zhuang, Y., & Alkatheiri, M. S. (2018, July). A Machine Learning-Based Security Vulnerability Study on XOR PUFs for Resource-Constraint Internet of Things. In 2018 IEEE International Congress on Internet of Things (ICIOT) (pp. 49-56). IEEE.

Citation Request:

The use of this dataset in publications should be acknowledged by referencing publication [1] listed above.

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