Classification of Keystroke Patterns for User Identification in a Pressure-Based Typing Biometrics System with Particle Swarm Optimization (PSO)
| Autor: | Ming Siong Soo, Imran Moez Khan, Beng Ghee Tan, Weng-Kin Lai |
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| Rok vydání: | 2015 |
| Předmět: | |
| Zdroj: | Neural Information Processing ISBN: 9783319265315 ICONIP (1) |
| DOI: | 10.1007/978-3-319-26532-2_9 |
| Popis: | Classification of users’ keystroke patterns captured from a typing biometrics system is discussed in this paper. Although the user identification system developed here requires the user to key-in their passwords as they would normally do, the identification of the users will only be based on their keystroke patterns rather than the actual passwords. The keystroke pattern generated is represented by the force applied on a numerical keypad and it is this set of features extracted from a common password that will be submitted to the classifiers to identify the different users. The typing biometrics system had been designed and developed with an 8-bit microcontroller that is based on the AVR enhanced RISC architecture. Classification of these keystroke patterns will be with PSO (particle swarm optimization) and this will be compared with the standard K-Means. The preliminary experimental results showed that the identity of users can be authenticated based solely on their keystroke biometric patterns from a numeric keypad. |
| Databáze: | OpenAIRE |
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