Syed Zulkarnain Syed Idrus

Soft biometrics for keystroke dynamics: Profiling individuals while typing passwords

This paper presents a new profiling approach of individuals based on soft biometrics for keystroke dynamics. Soft biometric traits are unique representation of a person, which can be in a form of physical, behavioural or biological human characteristics that differentiate between him/her into a group people (e.g. gender, age, height, colour, race etc.). Keystroke dynamics is a behavioural biometric modality to recognise how a person types on a keyboard. In this paper, we consider the following soft traits: the hand category (i.e. if the user types with one or two hands), the gender category, the age category and the handedness category. For this purpose, we collected a new database. Two cases are studied: static passwords and free text. By combining machine learning and fusion process, the results are promising.

Soft Biometrics for Keystroke Dynamics

Keystroke dynamics is a viable and practical way as an addition to security for identity verification. It can be combined with passphrases authentication resulting in a more secure verification system. This paper presents a new soft biometric approach for keystroke dynamics. Soft biometrics traits are physical, behavioral or adhered human characteristics, which have been derived from the way human beings normally distinguish their peers (e.g. height, gender, hair color etc.). Those attributes have a low discriminating power, thus not capable of identification performance. Additionally, they are fully available to everyone which makes them privacy-safe. Thus, in this study, it consists of extracting information from the keystroke dynamics templates with the ability to recognise the hand(s) used (i.e. one/two hand(s)); the gender; the age category; and the handedness of a user when he/she types a given password or passphrase on a keyboard. Experiments were conducted on a keystroke dynamics database of 110 users and our experimental results show that the proposed methods are promising.

Keystroke dynamics performance enhancement with soft biometrics

It is accepted that the way a person types on a keyboard contains timing patterns, which can be used to classify him/her, is known as keystroke dynamics. Keystroke dynamics is a behavioural biometric modality, whose performances, however, are worse than morphological modalities such as fingerprint, iris recognition or face recognition. To cope with this, we propose to combine keystroke dynamics with soft biometrics. Soft biometrics refers to biometric characteristics that are not sufficient to authenticate a user (e.g. height, gender, skin/eye/hair colour). Concerning keystroke dynamics, three soft categories are considered: gender, age and handedness. We present different methods to combine the results of a classical keystroke dynamics system with such soft criteria. By applying simple sum and multiply rules, our experiments suggest that the combination approach performs better than the classification approach with best result of 5.41% of equal error rate. The efficiency of our approaches is illustrated on a public database.

Complexity measurement of a password for keystroke dynamics: preliminary study

This paper discusses the complexity measurement of a password in relation to the performance of a keystroke dynamics system. The performance of any biometric system depends on the stability of the biometric data provided by the user. We first present a new way to calculate the complexity related to the typing of a password. This complexity metric is then validated with the keystroke dynamics data collected in an experiment, as well as the users experience during the experiment. Next, we show that the performance of the keystroke dynamics biometric system will depend on the complexity of the password and in particular that the performance of the system decreases with an increasing complexity. This leads then to the conclusion that random passwords might, although harder to guess by an attacker, might not be the most suitable choice in case of keystroke dynamics.

The Development of a Web-Based Claims System

The main purpose of this study is to develop a computer system for University Malaysia Perlis (UniMAP) staff to make claims via electronic media. Since this system is Web-based, staff can make claims anywhere, anytime and at any locations. This method can overcome not only human errors but also more efficient, fast and accurate. Therefore, this system can also save time, effort, and administrative costs. In this study, Active Server Pages (ASP) has been chosen to make the calculation and also to generate reports. After the system has been developed, a test was conducted using forms that have been simulated manually. The purpose is to enable the researcher to make comparison with the ones made using the developed system in order to detect errors or flaws from the manual simulation in the system.

Keystroke Dynamics for Construction Industry: A Review on Biometric Systems

Keystroke dynamics is known to be able to recognise a person associated with their way of typing on a computer keyboard. It is a fea-sible and useful method as an additional component to safety measures for identity verification. Previous studies show how keystroke dynamics can help to improve the recognition systems. Users behaviour when typ-ing, illustrates individual characteristics behind the computer screen. In this paper, we propose to use this application or technology along with a keypad to determine the construction workers’ attitude during work-ing hours, whether they have been working within a reasonable time or otherwise. This could possibly be a way to monitor their validity period while working. Keywords: Biometrics, keystroke dynamics, pattern recognition, construction.