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Generating Strictly Controlled Stimuli for Figure Recognition Experiments

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A parallel decision tree-based method for user authentication based on keystroke patterns.

Yong Sheng¹, Vir V Phoha, Steven M Rovnyak

¹Department of Electrical and Computer Engineering, Indiana University--Purdue University Indianapolis, Indianapolis, IN 46202-5132, USA. yosheng@iupui.edu

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society

|September 1, 2005

Summary

This study introduces a novel user authentication system using parallel decision trees (DTs) and keystroke patterns. The method effectively authenticates users with low false accept rates, enhancing security.

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Area of Science:

Computer Science
Cybersecurity
Biometrics

Background:

Traditional user authentication methods face challenges with evolving security threats.
Keystroke dynamics offer a unique biometric for continuous user authentication.
Generating sufficient training data for biometric systems can be resource-intensive.

Purpose of the Study:

To develop and evaluate a novel user authentication system based on keystroke patterns.
To improve the effectiveness of user authentication through data augmentation and ensemble methods.
To assess the performance of the proposed system using key security metrics.

Main Methods:

A Monte Carlo approach was used to generate simulated keystroke data, augmenting raw data.
Wavelet transforms were applied to create multiple training subsets from split data.
A parallel decision tree (DT) system, comprising eight DTs per user, was implemented for authentication.

Main Results:

The system achieved an average false accept rate (FAR) of 0.88%.
The average false reject rate (FRR) was recorded at 9.62%.
The parallel DT system demonstrated robust performance in authenticating users based on keystroke dynamics.

Conclusions:

The proposed Monte Carlo data generation and parallel DT approach is effective for keystroke-based user authentication.
The system shows promise for enhancing security in user identification systems.
Further research could explore real-time implementation and diverse typing patterns.