Authors:
Ahmed Wahab
and
Daqing Hou
Affiliation:
Electrical and Computer Engineering, Clarkson University, Potsdam, NY, USA 13699-5720, U.S.A.
Keyword(s):
Behavioral Biometrics, Keystroke Dynamics, Statistical Algorithms, Deep Neural Network, Siamese Network.
Abstract:
Keystroke dynamics has gained relevance over the years for its potential in solving practical problems like online fraud and account takeovers. Statistical algorithms such as distance measures have long been a common choice for keystroke authentication due to their simplicity and ease of implementation. However, deep learning has recently started to gain popularity due to their ability to achieve better performance. When should statistical algorithms be preferred over deep learning and vice-versa? To answer this question, we set up experiments to evaluate two state-of-the-art statistical algorithms: Scaled Manhattan and the Instance-based Tail Area Density (ITAD) metric, with a state-of-the-art deep learning model called TypeNet, on three datasets (one small and two large). Our results show that on the small dataset, statistical algorithms significantly outperform the deep learning approach (Equal Error Rate (EER) of 4.3% for Scaled Manhattan / 1.3% for ITAD versus 19.18% for TypeNet
). However, on the two large datasets, the deep learning approach performs better (22.9% & 28.07% for Scaled Manhattan / 12.25% & 20.74% for ITAD versus 0.93% & 6.77% for TypeNet).
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