A multi-technique approach for user identification through keystroke dynamics

Haider, Sajjad; Abbas, Abdulla; Zaidi, Abbas K.

doi:10.1109/icsmc.2000.886039

Cited by 80 publications

(46 citation statements)

References 6 publications

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“…These are fuzzy logic, neural networks and statistical techniques as well as combinations of these approaches. As shown in [8], of all the approaches statistical methods prove to be the most accurate. The statistical methods used can vary from comparing the digraphs of the entered text to the digraph means in the template, to classification methods, such as Bayes Classification algorithms [15] that use probability density functions.…”

Section: Emotions and Their Measurement Methodsmentioning

confidence: 99%

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Web-Based Biometric Mouse Intelligent System for Analysis of Emotional State and Labour Productivity

Zavadskas¹,

Kaklauskas²,

Seniut³

et al. 2008

ISARC 2008 - Proceedings From the 25th International Symposium on Automation and Robotics in Construction

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This paper describes the Web-based Biometric Mouse Intelligent (WBMI) System developed by the authors for measuring and analysis of user's emotions and labour productivity with a biometric mouse. The research included development of the WBMI System, which works in the background and is able to assess user's emotional state and labour productivity during work with a computer. The system captures information about user's emotional state and labour productivity using three main biometric techniques: physiological (skin conductance, amplitude of hand tremble, skin temperature), psychological (e-self-reports) and behavioural/motor-behavioural (mouse pressure, speed of mouse pointer movement, acceleration of mouse pointer movement, scroll wheel turns, right-and left-click frequency). The system extracts physiological and motorbehavioural parameters from mouse actions and palm characteristics, and the user fills in the psychological (e-self-reports) data, which can be used to analyse correlations with user's emotional state and labour productivity. Main features of the WBMI System are discussed, and the final recommendations for future research and improvement are included.

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Section: Emotions and Their Measurement Methodsmentioning

confidence: 99%

“…For example, as described in [8], there are three main keystroke analysis methods for the purposes of identity verification. These are fuzzy logic, neural networks and statistical techniques as well as combinations of these approaches.…”

Section: Emotions and Their Measurement Methodsmentioning

confidence: 99%

Web-Based Biometric Mouse Intelligent System for Analysis of Emotional State and Labour Productivity

Zavadskas¹,

Kaklauskas²,

Seniut³

et al. 2008

ISARC 2008 - Proceedings From the 25th International Symposium on Automation and Robotics in Construction

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show abstract

“…These results are not representative and are strictly limited to the dataset set used. In [7], the authors describe basically three multi-technique approaches for user identification through keystroke dynamics. That is, they developed software consisting of a neural net unit, a fuzzy unit and a statistical unit.…”

Section: Related Workmentioning

confidence: 99%

How to comprehensively describe a biometric update mechanisms for keystroke dynamics

Seeger

Bours²

2011

2011 Third International Workshop on Security and Communication Networks (IWSCN)

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Abstract-Performance of biometric keystroke dynamics systems improve when using update mechanisms for the template. Generally, only the results are reported in literature but no description of how the results were achieved is presented. In this paper, we identify not only the parameters related to the update mechanism, but also the parameters for the testing of the update mechanism. For two selected update mechanisms, we will show that the performance of those mechanisms heavily depend on the used method for evaluation. For one of these update mechanisms the final FMR value varied from 12.85% to 53.60% depending on the evaluation method. This clearly shows that performance results on update mechanisms by themselves have no value unless the method of evaluation is described.

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“…In [32], a suite of techniques for password authentication uses neural networks (3-layer feedforward network implementing with the back propagation algorithm), fuzzy logic (centre of gravity), and statistical methods (average and standard deviation) to solve the same problem.…”

Section: Background and Related Workmentioning

confidence: 99%

Keystroke Authentication on Enhanced Needleman Alignment Algorithm

Bamatraf¹,

Bamatraf²,

Hegazy³

2014

IIM

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An important point for computer systems is the identification of users for authentication. One of these identification methods is keystroke dynamics. The keystroke dynamics is a biometric measurement in terms of keystroke press duration and keystroke latency. However, several problems are arisen like the similarity between users and identification accuracy. In this paper, we propose innovative model that can help to solve the problem of similar user by classifying user's data based on a membership function. Next, we employ sequence alignment as a way of pattern discovery from the user's typing behaviour. Experiments were conducted to evaluate accuracy of the proposed model. The results show high performance compared to standard classifiers in terms of accuracy and precision.

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A multi-technique approach for user identification through keystroke dynamics

Cited by 80 publications

References 6 publications

Web-Based Biometric Mouse Intelligent System for Analysis of Emotional State and Labour Productivity

Web-Based Biometric Mouse Intelligent System for Analysis of Emotional State and Labour Productivity

How to comprehensively describe a biometric update mechanisms for keystroke dynamics

Keystroke Authentication on Enhanced Needleman Alignment Algorithm

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