Abstract
AbstractCurrent approaches to user authentication via keystroke dynamics are based on either key-pressed durations and multiple key latencies [2] or key-pressed forces to find personal typing motif ([3], [4]). This paper proposes a novel method to detect key presses, release durations as well as key-pressed forces indirectly through analyzing sound signals created when typing the keyboard. Both above sources of information are represented in the proposed keystroke dynamics bio-matrix. A personal keystroke dynamics bio-matrix is used to train a fuzzy neural network ([1], [5]) to solve user authentication problem. Experimental results show that the proposed method are feasible and reliable with false acceptance rate (FAR) 3.5% and false rejection rate (FRR) 7%.KeywordsFuzzy RuleUser AuthenticationSound SignalFuzzy Neural NetworkLabel NodeThese 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.
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