Keystroke Dynamics in a General Setting

Janakiraman, Rajkumar; Sim, Terence

doi:10.1007/978-3-540-74549-5_62

Cited by 41 publications

(20 citation statements)

References 10 publications

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“…Static version occurs generally before the authentication process can begin and that is before enrollment. In a dynamic version of enrollment, the users enroll during and/or after they have gained access to the system [9]- [11]. In our work the static version of enrollment is used.…”

Section: Introductionmentioning

confidence: 99%

Personal Authentication Based on Keystroke Dynamics Using Soft Computing Techniques

Karnan¹,

Akila

2010

2010 Second International Conference on Communication Software and Networks

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The need to secure sensitive data and computer systems from intruders, while allowing ease of access for authenticate user is one of the main problems in computer security. Traditionally, passwords have been the usual method for controlling access to computer systems but this approach has many inherent flaws. Keystroke dynamics is a promising biometric technique to recognize an individual based on an analysis of his/her typing patterns. In the experiment, we measure mean, standard deviation and median values of keystroke features such as latency, duration, digraph and their combinations and compare their performance. Particle swarm optimization (PSO), genetic algorithm (GA) and the proposed ant colony optimization (ACO) are used for feature subset selection. Back propagation neural network (BPNN) is used for classification. ACO gives better performance than PSO and GA with regard to feature reduction rate and classification accuracy. Using digraph as the feature for feature subset selection is novel and show good classification performance.

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Section: Introductionmentioning

confidence: 99%

Personal Authentication Based on Keystroke Dynamics Using Soft Computing Techniques

Karnan¹,

Akila

2010

2010 Second International Conference on Communication Software and Networks

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“…The characteristics are represented by key events, i.e. pressing and releasing of a key as well as corresponding hold and transition periods [1,26]. Since these events can be measured in the millisecond range it is not possible to simulate another person's keystroke dynamics [10,34].…”

Section: A Methods and System Descriptionmentioning

confidence: 99%

Biometric authentication as a service for enterprise identity management deployment: a data protection perspective

Senk

Dotzler

2011

2011 Sixth International Conference on Availability, Reliability and Security

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Biometric Authentication as a Service is an innovative approach for strong authentication in web environments based on the Software as a Service model. However, both the adoption of SaaS systems and biometric technologies negatively correlate with perceived privacy and data protection risks. We specify a list of evaluation criteria for BioAaaS systems from a data protection point of view including elements specific to both biometrics and SaaS. We further apply these criteria on a prototypical implementation of a SaaS-compliant biometric authentication service based on keystroke dynamics for enterprise deployment. The assessment shows that for the most part the prototype conforms to technical data protection requirements. At the organizational level the selection and control of a trust-worthy provider and the conclusion of the service agreement remain.

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“…Here system records all the key press and release timing and calculates the duration of depressed characters, latency time between various down and up key sequence latencies for each sample, then fmds out the actual timing template by applying some statistical methods. Then some features mining mechanism or distance based algorithm such as Euclidean distance, Manhattan distance, Manhattan distance with standard deviation, Mahalanobis distance, Bhattacharyya Distance as per Janakiraman, R. and Sim, T. [14], or Genetic algorithm, particle swan optimisation which is explained by Marcus, K. and Akila, M. [15] may be used to decide whether the user is valid. Thus we can minimise the probability of any off-line guessing attacks as rhythm of password is used, which cannot be copied even after watching it several times.…”

Section: Keystrokementioning

confidence: 99%

Free-text user authentication technique through Keystroke Dynamics

Roy

Sinha

2014

2014 International Conference on High Performance Computing and Applications (ICHPCA)

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Some common words (name, address, E-mail ID, ... ), we press daily and we are habituated to press it in same rhythm, which is unique and can be used to segregate and distinguish people. In this paper we are considering rhythm not only the entered common words or some sequence of common characters. Here machine intelligence relies on the fact that it stores the typing style of some daily used words which are supported by the user and can be used as a secret key. Recognising typing style promises a parameter like behavioural biometric characteristics that may facilitate non-intrusive, cost effective and continuous monitoring. But this technique, as of now, suffers from accuracy level and performance. In order to realize, this technique in practice a higher level of security and performance together with low cost version is needed with an error to an accepted level. Hence, it is highly essential to identify the controlling parameters and optimise the accuracy and performance as well as cost with new algorithms.

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Keystroke Dynamics in a General Setting

Cited by 41 publications

References 10 publications

Personal Authentication Based on Keystroke Dynamics Using Soft Computing Techniques

Personal Authentication Based on Keystroke Dynamics Using Soft Computing Techniques

Biometric authentication as a service for enterprise identity management deployment: a data protection perspective

Free-text user authentication technique through Keystroke Dynamics

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