Keystroke Dynamics Extraction by Independent Component Analysis and Bio-matrix for User Authentication

Abstract

AbstractKeystroke dynamics is unique specific characteristics used for user authentication problem. There are many researches to detect personal keystroke dynamics and authenticate user based on these characteristics. Most researches study on either the key press durations and multiple key latencies (typing time) or key-pressed forces (pressure-based typing) to find the owned personal motif (unique specific characteristic). This paper approaches to extract keystroke dynamics by using independent component analysis (ICA) through a standardized bio-matrix from typing sound signals which contain both typing time and typing force information. The ICA representation of keystroke dynamics is effective for authenticating user in our experiments. The experimental results show that the proposed keystroke dynamics extraction solution is feasible and reliable to solve user authentication problem with false acceptance rate (FAR) 4.12% and false rejection rate (FRR) 5.55%.KeywordsFeature VectorFace RecognitionIndependent Component AnalysisUser AuthenticationIndependent Component AnalysisThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.