An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Enhanced Password Authentication through Fuzzy Logic
IEEE Expert: Intelligent Systems and Their Applications
Information Sciences: an International Journal
Typing Biometric Keypads: Combining Keystroke Time and Pressure Features to Improve Authentication
Journal of Organizational and End User Computing
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Security of an information system depends to a large extent on its ability to authenticate legitimate users as well as to withstand attacks of various kinds. Confidence in its ability to provide adequate authentication is, however, waning. This is largely due to the wrongful use of passwords by many users. In this paper, the design and development of keystroke pressure-based typing biometrics for individual user's verification which based on the analysis of habitual typing of individuals is discussed. The combination of maximum pressure exerted on the keyboard and time latency between keystrokes is used as features to create typing patterns for individual users so as to recognize authentic users and to reject impostors. Support vector machines (SVMs), which is relatively new machine learning, is used as a pattern matching method. The effectiveness of the proposed system is evaluated based upon False Reject Rate (FRR) and False Accept Rate (FAR). A series of experiment shows that the proposed system is effective for biometric-based security system.