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Performance counter dataset for behavioural biometric purpose.

Cesar Andrade¹, Hendrio Bragança¹, Eduardo Feitosa¹

¹Institute of Computing, Federal University of Amazonas, Amazonas, Brazil.

|January 16, 2024

Summary

This summary is machine-generated.

We present two new Windows performance counter datasets, COUNT-OS-I and COUNT-OS-II, to advance continuous user authentication research. These datasets offer real-world data for developing robust authentication models and enhancing system security.

Keywords:

Behavioural biometrics Continuous authentication Machine learning Performance counters

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Area of Science:

Computer Science
Cybersecurity
Data Science

Background:

Continuous user authentication is crucial for modern cybersecurity.
Existing datasets may lack real-world diversity or comprehensive metrics.
Advancing authentication models requires high-quality, representative data.

Purpose of the Study:

Introduce COUNT-OS-I and COUNT-OS-II, novel Windows performance counter datasets.
Provide rich, real-world data for developing and evaluating continuous user authentication models.
Establish reliable benchmarks to foster innovation in user authentication.

Main Methods:

Collected performance counter data from 63 Windows computers and users.
COUNT-OS-I: 26 users, diverse environments, 159 attributes, ~26h/user.
COUNT-OS-II: 37 users, identical configurations, 218 attributes, 48h.
Employed pseudonymization to protect user privacy.

Main Results:

Datasets offer comprehensive, statistically validated performance metrics.
COUNT-OS-I and COUNT-OS-II capture diverse user and system interactions.
Data is well-balanced, suitable for rigorous model training and testing.

Conclusions:

COUNT-OS-I and COUNT-OS-II are valuable resources for continuous user authentication research.
These datasets will facilitate the development of more robust and trustworthy authentication systems.
The release aims to accelerate progress in securing user access and enhancing digital trust.