A Survey of behavioral authentication using keystroke dynamics: Touch screens and mobile devices

Authentication is a way to enable an individual to be uniquely identified usually based on passwords and personal identification number (PIN). The main problems of such authentication techniques are the unwillingness of the users to remember long and challenging combinations of numbers, letters, and symbols that can be lost, forged, stolen, or forgotten. In this paper, we investigate the current advances in the use of behavioral-based biometrics for user authentication. The application of behavioral-based biometric authentication basically contains three major modules, namely, data capture, feature extraction, and classifier. This application is focusing on extracting the behavioral features related to the user and using these features for authentication measure. The objective is to determine the classifier techniques that mostly are used for data analysis during authentication process. From the comparison, we anticipate to discover the gap for improving the performance of behavioral-based biometric authentication. Additionally, we highlight the set of classifier techniques that are best performing for behavioral-based biometric authentication.

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“…Knowledge-based technique is commonly used to secure the access for systems [14]. The two famous examples are the pin and password.…”

Section: Knowledge-based Methodsmentioning

confidence: 99%

“…The use of human characteristics is the best solution compared to the user that personally knows and possesses [14]. In other words, biometric-based method cannot be forgotten or lost in contrast to token, smart card, and password [24].…”

Section: Biometric-based Methodsmentioning

confidence: 99%

A Survey of Machine Learning Techniques for Behavioral-Based Biometric User Authentication

Mahadi¹,

Mohamed²,

IhsanMohamad³

et al. 2018

Recent Advances in Cryptography and Network Security

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“…A dedicated dataset for mobile and touch screen devices are still few in literature and they were not covering all the provided non-timing features we mentioned in the literature. So, we are in bad needs for public touch screen devices benchmarks [20][21][22][23][24][25][26][27][28].…”

Section: Benchmarking Datasets and Challengesmentioning

confidence: 99%

“…The notation of MLP classifier (x,y,z) represents the number of neurons in each layer with three layers structure. For example, if we used the notation MLP (10,10,20) to represent MLP structure; then we have three layer with 10, 10, and 20 nodes in each layer respectively. Fig.…”

Section: Neural Network Classifier (Nn)mentioning

confidence: 99%

Enhanced Authentication System Performance Based on Keystroke Dynamics using Classification algorithms

Salem¹,

Sharieh²,

Sleit³

et al. 2019

KSII TIIS

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Nowadays, most users access internet through mobile applications. The common way to authenticate users through websites forms is using passwords; while they are efficient procedures, they are subject to guessed or forgotten and many other problems. Additional multi modal authentication procedures are needed to improve the security. Behavioral authentication is a way to authenticate people based on their typing behavior. It is used as a second factor authentication technique beside the passwords that will strength the authentication effectively. Keystroke dynamic rhythm is one of these behavioral authentication methods. Keystroke dynamics relies on a combination of features that are extracted and processed from typing behavior of users on the touched screen and smart mobile users. This Research presents a novel analysis in the keystroke dynamic authentication field using two features categories: timing and no timing combined features. The proposed model achieved lower error rate of false acceptance rate with 0.1%, false rejection rate with 0.8%, and equal error rate with 0.45%. A comparison in the performance measures is also given for multiple datasets collected in purpose to this research.

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“…Saini, Kaur, and Bhatia [96] and Saifan et al [95] presented a comprehensive review on keystroke dynamics research -especially for touch screen and mobile devices. But these papers are not up to date as these were published in 2016.…”

mentioning

confidence: 99%

Keystroke Dynamics: Concepts, Techniques, and Applications

Shadman¹,

Wahab²,

Manno³

et al. 2023

Preprint

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Reliably identifying and authenticating users remains integral to computer system security. Various novel authentication tenchniques such as biometric authentication systems have been devised in recent years. This paper surveys keystroke-based authentication systems and their applications such as continuous authentication. Keystroke dynamics promises to be non-intrusive and cost-effective as no addition hardware is required other than a keyboard. This survey can be a reference for researchers working on keystroke dynamics.

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A Survey of behavioral authentication using keystroke dynamics: Touch screens and mobile devices

Cited by 7 publications

References 17 publications

A Survey of Machine Learning Techniques for Behavioral-Based Biometric User Authentication

A Survey of Machine Learning Techniques for Behavioral-Based Biometric User Authentication

Enhanced Authentication System Performance Based on Keystroke Dynamics using Classification algorithms

Keystroke Dynamics: Concepts, Techniques, and Applications

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