Continuous Typist Verification using Machine Learning

摘要: A keyboard is a simple input device. Its function to send keystroke information the computer (or other device) which it attached. Normally this employed solely produce text, but can also be utilized as part of an authentication system. Typist verification exploits typist’s patterns check whether they are who say are, even after standard schemes have confirmed their identity. This thesis investigates typists behave in sufficiently unique yet consistent manner enable effective level based on typing patterns. depends more than behaviour. The quality and algorithms used compare them determine how accurately performed. sheds light all technical aspects problem, including data collection, feature identification extraction, sample classification. dataset has been collected that comparable size, timing accuracy content others field, with one important exception: derived from real emails, rather samples artificial setting. gain insight into what features distinguish another. train learning make judgements origin previously unseen samples. These use “one-class classification”; predictions for particular user having trained only user’s examines many one-class classification algorithms, ones designed specifically typist verification. New proposed increase speed accuracy. best method performs at state art terms accuracy, while decreasing time taken prediction minutes seconds, and—more importantly—without requiring any negative users. Also, general: applies not verification, problem. Overall, concludes considered useful biometric technique.