@inproceedings{8db8302ae6bd4cea8b342c355865a896,
title = "Sensitivity analysis in keystroke dynamics using convolutional neural networks",
abstract = "Biometrics has become ubiquitous and spurred common use in many authentication mechanisms. Keystroke dynamics is a form of behavioral biometrics that can be used for user authentication while actively working at a terminal. The proposed mechanisms involve digraph, trigraph and n-graph analysis as separate solutions or suggest a fusion mechanism with certain limitations. However, deep learning can be used as a unifying machine learning technique that consolidates the power of all different features since it has shown tremendous results in image recognition and natural language processing. In this paper, we investigate the applicability of deep learning on three different datasets by using convolutional neural networks and Gaussian data augmentation technique. We achieve 10\% higher accuracy and 7.3\% lower equal error rate (EER) than existing methods. Also, our sensitivity analysis indicates that the convolution operation and the fully-connected layer are the most prominent factors that affect the accuracy and the convergence rate of a network trained with keystroke data.",
keywords = "Behavioral biometrics, convolutional neural network, data augmentation, deep learning, keystroke dynamics",
author = "Hayreddin Ceker and Shambhu Upadhyaya",
note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 2017 IEEE Workshop on Information Forensics and Security, WIFS 2017 ; Conference date: 04-12-2017 Through 07-12-2017",
year = "2017",
month = jul,
day = "2",
doi = "10.1109/WIFS.2017.8267667",
language = "English",
series = "2017 IEEE Workshop on Information Forensics and Security, WIFS 2017",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1--6",
booktitle = "2017 IEEE Workshop on Information Forensics and Security, WIFS 2017",
address = "United States",
}