Keystroke Biometrics Studies on Short Password and Numeric Passcode Input, and on Long Spreadsheet, Browser, and Text Application Input.

摘要

A keystroke biometric system was enhanced to capture raw keystroke data directly from an individual's computer system using an open source key logger originally designed for software testing. The key logger runs in the background capturing keystrokes directly through the operating system requiring no additional capture software, text entry window, or edit box for input. This allows the user freedom to generate unrestricted keystroke entry from any application installed on their system.;Long input data were collected from 20 participants using spreadsheet, browser, and text applications. Participants were free to type whatever they desired without using copy tasks in any of these experimental scenarios. Verification experiments were run on these samples using two classifiers. The newer Multi Match was far superior to the older Single Match classifier yielding EER performance of 8.1%, 15.7%, and 5.8% for spreadsheet, browser, and text entry in comparison to 13.6%, 27.5%, and 12.8%, respectively, for the older Single Match.;Short input data simulating a ten digit passcode were collected from 30 users entering the digits from the numeric keypad section of the keyboard. Using the feature set from the previous experiments, results were obtained from both classifiers - the EER performance using the Multi Match, varying the participants from 10 to 20 to 30, were 5.5%, 5.7%, and 6.1% compared to 15.6%, 15.7%, and 15.0%, respectively, from the Single Match classifier. Additional short-input experiments were run using data and features from Carnegie Melon University (CMU). The first was another keypad experiment using Pace University data with the CMU feature set and the second was a password experiment using both data and features from CMU.;The experiments conducted in this study had various independent variables, including participant count, classifier, feature set, and content type. Additionally, the data samples from the long input experiments were analyzed to get a better understanding of the performance variances and how they relate to keystroke lengths by calculating keystroke densities as the number of keystrokes divided by data capture elapse time.