A Survey of Keystroke Dynamics Biometrics <s> Customized Hardware. <s> Mobile handsets have found an important place in modern society, with hundreds of millions currently in use. The majority of these devices use inherently weak authentication mechanisms, based upon passwords and PINs. This paper presents a feasibility study into a biometric-based technique, known as keystroke analysis – which authenticates the user based upon their typing characteristic. In particular, this paper identifies two typical handset interactions, entering telephone numbers and typing text messages, and seeks to authenticate the user during their normal handset interaction. It was found that neural network classifiers were able to perform classification with average equal error rates of 12.8%. Based upon these results, the paper concludes by proposing a flexible and robust framework to permit the continuous and transparent authentication of the user, thereby maximising security and minimising user inconvenience, to service the needs of the insecure and evermore functional mobile handset. <s> BIB001 </s> A Survey of Keystroke Dynamics Biometrics <s> Customized Hardware. <s> This paper presents the development of a keystroke dynamics-based user authentication system using the ARTMAP-FD neural network. The effectiveness of ARTMAP- FD in classifying keystroke patterns is analyzed and compared against a number of widely used machine learning systems. The results show that ARTMAP-FD performs well against many of its counterparts in keystroke patterns classification. Apart from that, instead of using the conventional typing timing characteristics, the applicability of typing pressure to ascertaining user's identity is investigated. The experimental results show that combining both latency and pressure patterns can improve the equal error rate (ERR) of the system. <s> BIB002 </s> A Survey of Keystroke Dynamics Biometrics <s> Customized Hardware. <s> In everyday life many actions are secured and/or authenticated by use of a secret personal identification number (PIN). This paper reports an improvement to the security afforded by a numeric PIN system through the addition of a covert biometric identification process. This biometric identification process is achieved through the use of a modified keypad incorporating force sensors within the individual keys, these sensors permit the dynamic measurement of applied force and key-press timings, providing an advantage over existing keystroke dynamic techniques which rely on time-domain information only. Details of the experimental setup used are given, and the results from initial tests are reported. In these tests an equal error rate of approximately 10% is obtained and a false acceptance rate of15% with a corresponding false rejection rate of0% has been shown. <s> BIB003 </s> A Survey of Keystroke Dynamics Biometrics <s> Customized Hardware. <s> The purpose of this study is to evaluate the performance of static keystroke analysis as a complimentary and transparent security solution in the context of perimeter defense and facility access. A biometric keypad prototype was developed to evaluate the classification accuracy using temporal keystroke characteristics and feature points extracted from pressure sensors embedded within the keypad. A novel method is proposed to improve keystroke classification accuracy by analyzing 3 pressure related characteristics: amplitude or peak, peak area, and peak sharpness. <s> BIB004 </s> A Survey of Keystroke Dynamics Biometrics <s> Customized Hardware. <s> 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. Radial basis function network (RBFN), which is one of the artificial neural network, 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. <s> BIB005 </s> A Survey of Keystroke Dynamics Biometrics <s> Customized Hardware. <s> 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 paper examines the use of maximum pressure exerted on the keyboard and time latency between keystrokes as features to create typing patterns for individual users. Combining both an Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) are adopted as classifiers to verify the authorized and unauthorized users based on extracted features of typing biometric. 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 that used combined classifiers produces promising result for both FAR and FRR. <s> BIB006
Conventional input devices such as normal computer keyboards are only capable of producing keystroke timing vector as feature data for analysis. A secondary feature data that may be proven more distinctive is the pressure sequence while interacting with the input devices. Therefore, numerous researchers have tried to modify the existing devices BIB005 BIB001 BIB006 BIB002 to include pressure sensitive receivers. Another modification was made to a rubber membrane keypad that resembles an ATM machine input alignment BIB003 , with the objective of improving security on a numeric PIN system. The original mounted printed circuit board underneath the keypad was replaced by custom fabricated force sensitive resistors. However, the actual implementation to the banking sector is rather doubtful due to the cost of replacement to the entire hardware infrastructure. Leberknight et al. BIB004 pointed out that leveraging the effects of soft and hard key presses was crucial yet challenging for tailored made pressure sensitive devices. Parasitic capacitive coupling that occurs in over sensitive devices might distort feature quality. This raised the concern that a minimal benchmark on the accuracy of pressure input devices might be required if it is to be used in large-scale applications. However, we foresee that in the post-pc era  , pressure sensitivity standards in personal digital devices will be able to meet the practical needs.