Double serial adaptation mechanism for keystroke dynamics authentication based on a single password

Mhenni, Abir; Cherrier, Estelle; Rosenberger, Christophe; Amara, Najoua Essoukri Ben

doi:10.1016/j.cose.2019.02.002

Cited by 28 publications

(11 citation statements)

References 48 publications

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“…Biometrics is one of the safest and most reliable ways of authentication as it is the characteristic of the user only. The most popular biometric methods used are finger scan [4], [5], retina scan [6], face scan [7], [8], keystroke dynamics [9], [10], etc. Finger scan is the most popular biometric being used in mobile phones.…”

Section: Introductionmentioning

confidence: 99%

A Three-Step Authentication Model for Mobile Phone User Using Keystroke Dynamics

et al. 2020

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The use of keystroke dynamics for user authentication has evolved over the years and has found its application in mobile phones. But the primary challenge with mobile phones is that they can be used in any position. Thus, it becomes critical to analyze the use of keystroke dynamics using the data collected in various typing positions. This research proposed a three-step authentication model that could be used to authenticate a user who is using the mobile in sitting, walking, and relaxing position. Furthermore, the mobile orientation (portrait and landscape) was considered while taking input from the user. Apart from using traditional keystroke features, accelerometer data were also combined for classification using Random Forest(RF) and K-Nearest Neighbour(KNN) classifiers. The three-step authentication method was able to authenticate a user with an EER of 2.9% for the relaxing landscape position. Finally, the model was optimized using Particle Swarm Optimization (PSO) to reduce the feature set and make the model more practical for mobile phones. Optimization helped to reduce the number of features from 55 to 17 and improved the EER to 2.2%. The research validated that relaxing and walking positions are the best positions to authenticate a user using keystroke dynamics.

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Section: Introductionmentioning

confidence: 99%

A Three-Step Authentication Model for Mobile Phone User Using Keystroke Dynamics

et al. 2020

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“…2. The short texts datasets are predominantly based on passwords thereby mostly appropriate for studying the static authentication [9]. On the other hand, the long texts datasets are further divided into two categories i.e., fixed text and free text.…”

Section: Literature Reviewmentioning

confidence: 99%

Continuous User Authentication Featuring Keystroke Dynamics Based on Robust Recurrent Confidence Model and Ensemble Learning Approach

et al. 2020

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User authentication is considered to be an important aspect of any cyber security program. However, one-time validation of user's identity is not strong to provide resilient security throughout the user session. In this aspect, continuous monitoring of session is necessary to ensure that only legitimate user is accessing the system resources for entire session. In this paper, a true continuous user authentication system featuring keystroke dynamics behavioural biometric modality has been proposed and implemented which tends to authenticate the user on each action. The proposed 2-phase methodology, consisting of ensemble learning and robust recurrent confidence model, decides the genuineness of user based on each action and locks out the imposter user based on final threshold, however, it tends to lock out imposter user more quickly if it reaches the alert threshold. Moreover, system has been validated with two different experimental settings and results are reported in terms of mean average number of genuine actions (ANGA) and average number of imposter actions(ANIA), whereby achieving the lowest mean ANIA with experimental setting II.

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“…The current authentication mechanisms, which have been used in most applications, vary from using a single factor, for example, a password or user ID, to using a multi-factor authentication, such as a smart card or biological characteristic. These traditional methods are not effective in providing appropriate protection and can cause various issues and damage, for example, recently passwords have been easily and frequently hacked [88]. Multi-factor authentication relies on centralisation or trusting third-party services, which, as we discussed previously, have high security risks.…”

Section: Authenticationmentioning

confidence: 99%

An Internet of Things and Blockchain Based Smart Campus Architecture

Alkhammash

Beloff

White

2020

Advances in Intelligent Systems and Computing

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Rapid development in science and information technologies, such as the Internet of things, has led to a growth in the number of studies and research papers on smart cities in recent years and more specifically on the construction of smart campus technologies. This paper will review the concept of a smart campus, discuss the main technologies deployed, and then propose a new novel framework for a smart campus. The architecture of this new smart campus approach will be discussed with particular consideration of security and privacy systems, the Internet of things, and blockchain technologies.

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Double serial adaptation mechanism for keystroke dynamics authentication based on a single password

Cited by 28 publications

References 48 publications

A Three-Step Authentication Model for Mobile Phone User Using Keystroke Dynamics

A Three-Step Authentication Model for Mobile Phone User Using Keystroke Dynamics

Continuous User Authentication Featuring Keystroke Dynamics Based on Robust Recurrent Confidence Model and Ensemble Learning Approach

An Internet of Things and Blockchain Based Smart Campus Architecture

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