Dynamic User Authentication Based on Mouse Movements Curves

Hinbarji, Zaher; Albatal, Rami; Gurrin, Cathal

doi:10.1007/978-3-319-14442-9_10

Cited by 22 publications

(5 citation statements)

References 8 publications

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“…Finally, the planned model proved that the EER decreases with an increasing detection time. The verification system based on mouse movement curves are proposed by Hinbarji et al (2015). It collects the data from 10 user's mouse movements and is disintegrated into a collection of curves.…”

Section: Mouse Dynamicsmentioning

confidence: 99%

A broad review on non-intrusive active user authentication in biometrics

Thomas¹,

Mathew

2021

J Ambient Intell Human Comput

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Authentication is the process of keeping the user's personal information as confidential in digital applications. Moreover, the user authentication process in the digital platform is employed to verify the own users by some authentication methods like biometrics, voice recognition, and so on. Traditionally, a one-time login based credential verification method was utilized for user authentication. Recently, several new approaches were proposed to enhance the user authentication framework but those approaches have been found inconsistent during the authentication execution process. Hence, the main motive of this review article is to analyze the advantage and disadvantages of authentication systems such as voice recognition, keystroke, and mouse dynamics. These authentication models are evaluated in a continuous non-user authentication environment and their results have been presented in way of tabular and graphical representation. Also, the common merits and demerits of the discussed authentication systems are broadly explained discussion section. Henceforth, this study will help the researchers to adopt the best suitable method at each stage to build an authentication framework for non-intrusive active authentication.

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Section: Mouse Dynamicsmentioning

confidence: 99%

A broad review on non-intrusive active user authentication in biometrics

Thomas¹,

Mathew

2021

J Ambient Intell Human Comput

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“…Hinbarji et al . [16] collected a general mouse usage data set from 10 subjects. They used mouse movement curves (actions) as the basic modelling unit for user authentication.…”

Section: Background and Related Workmentioning

confidence: 99%

Intrusion detection using mouse dynamics

Antal¹,

Egyed-Zsigmond

2019

IET biom.

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Compared to other behavioural biometrics, mouse dynamics is a less explored area. General purpose data sets containing unrestricted mouse usage data are usually not available. The Balabit data set was released in 2016 for a data science competition which, despite the few users covered, can be considered the first adequate publicly available data set. This study presents a performance evaluation study on this data set for impostor detection. The existence of very short benchmark test sessions makes this data set challenging. Raw data were segmented into mouse actions, such as mouse move, point and click, and drag and drop, and then several features were extracted. Mouse data is not sensitive; therefore, it is possible to collect negative mouse dynamics data and to use two-class classifiers. Impostor detection performance was measured using decisions based on several actions. The best performance of 0.92 area under curve was obtained on the benchmark test sessions of the data set. Drag and drop mouse actions proved to be the best actions for impostor detection despite the fact that this type of action represented only 10% of the data set.

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“…Like Zheng at al. [11], [12], Hinbarji et al [15] used mouse movement curves as the basic modelling unit for user authentication. They obtained 9.8% EER by using features extracted from 100 mouse curves (approximately 5.6 minutes length mouse data).…”

Section: Background and Related Workmentioning

confidence: 99%

Intrusion Detection Using Mouse Dynamics

Antal¹,

Egyed-Zsigmond²

2018

Preprint

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Compared to other behavioural biometrics, mouse dynamics is a less explored area. General purpose data sets containing unrestricted mouse usage data are usually not available. The Balabit data set was released in 2016 for a data science competition, which against the few subjects, can be considered the first adequate publicly available one. This paper presents a performance evaluation study on this data set for impostor detection. The existence of very short test sessions makes this data set challenging. Raw data were segmented into mouse move, point and click and drag and drop types of mouse actions, then several features were extracted. In contrast to keystroke dynamics, mouse data is not sensitive, therefore it is possible to collect negative mouse dynamics data and to use two-class classifiers for impostor detection. Both action-and set of actions-based evaluations were performed. Set of actions-based evaluation achieves 0.92 AUC on the test part of the data set. However, the same type of evaluation conducted on the training part of the data set resulted in maximal AUC (1) using only 13 actions. Drag and drop mouse actions proved to be the best actions for impostor detection.

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Dynamic User Authentication Based on Mouse Movements Curves

Cited by 22 publications

References 8 publications

A broad review on non-intrusive active user authentication in biometrics

A broad review on non-intrusive active user authentication in biometrics

Intrusion detection using mouse dynamics

Intrusion Detection Using Mouse Dynamics

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