Introducing touchstroke: keystroke‐based authentication system for smartphones

Kambourakis, Georgios; Damopoulos, Dimitrios; Papamartzivanos, Dimitrios; Pavlidakis, Emmanouil

doi:10.1002/sec.1061

Cited by 93 publications

(58 citation statements)

References 30 publications

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“…These sensors are capable of providing raw data with high precision and accuracy and are very useful in monitoring the three-dimensional device movements and positioning [18]. In particular, accelerometer readings are used by Grant Ho [19] as it is believed that user have natural orientation while holding the phone during usage.…”

Section: Touch Dynamics For Smart Phonesmentioning

confidence: 99%

Performance Analysis for a Touch Dynamic Authentication System with Reduced Feature Set Using Neural Networks

SenthilPrabha

Vidhyapriya

2015

IETE Journal of Research

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Increase in the usage of smart phones increases the burden for the users to memorize many passwords. It has also increased the need for stronger or enhanced authentication mechanisms. Littlemore innovation labs are providing paperless solution for conducting examinations using touch pad that is believed to be a promising technology in future. This paper explores the suitability of using touch dynamics as an additional level of security during authentication. Both security and performance concerns are investigated. A security application developed can be used on a touch screen device capable of imitating someone's typing characteristics. When the user starts interacting with the device, the developed application starts capturing the behavioural features given by the user's swipe. Thirty different features are captured, from which, most prominent features are identified by depending on the usefulness of the feature. Good results with the reduced feature set are obtained, thereby improving the performance of the system using neural network techniques. The results show that touch dynamics on a smart phone are more durable against certain attacks on a personal computer.

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Section: Touch Dynamics For Smart Phonesmentioning

confidence: 99%

Performance Analysis for a Touch Dynamic Authentication System with Reduced Feature Set Using Neural Networks

SenthilPrabha

Vidhyapriya

2015

IETE Journal of Research

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“…Using speed of finger movement: Kambourakis et al [51] introduced a method based on the speed and distance of finger movement on touchscreen devices. This method aims to improve analysis of traditional keystroke dynamics.…”

Section: )mentioning

confidence: 99%

Authentication of Smartphone Users Using Behavioral Biometrics

Alzubaidi

Kalita

2016

IEEE Commun. Surv. Tutorials

168

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Smartphones and tablets have become ubiquitous in our daily lives. Smartphones, in particular, have become more than personal assistants. These devices have provided new avenues for consumers to play, work and socialize whenever and wherever they want. Smartphones are small in size; so they are easy to handle and to stow and carry in users' pockets or purses. However, mobile devices are also susceptible to various problems. One of the greatest concerns is the possibility of breach in security and privacy if the device is seized by an outside party. It is possible that threats can come from friends as well as strangers. Due to the size of smart devices, they can be easily lost and may expose details of users' private lives. In addition, this might enable pervasive observation or imitation of one's movements and activities, such as sending messages to contacts, accessing private communication, shopping with a credit card, and relaying information about where one has been. This paper highlights the potential risks that occur when smartphones are stolen or seized, discusses the concept of continuous authentication, and analyzes current approaches and mechanisms of behavioral biometrics with respect to methodology, associated datasets and evaluation approaches.

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“…They found that despite small performance degradation, software keyboard input still provides feasible features for keystroke dynamics biometric authentication. Kambourakis et al [58] proposed to enhance traditional keystroke dynamics features with additional data on the speed and distance of finger movement on smartphones with touchscreens. This upgrade resulted in an EER of 26% on a 10-digit PIN and an EER of 13.6% on short passphrases.…”

Section: Keystroke Dynamics For Mobile Devicesmentioning

confidence: 99%

A Survey on Keystroke Dynamics Biometrics: Approaches, Advances, and Evaluations

Zhong¹,

Deng²

2015

Gate to Computer Science and Research

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In this review paper we present a comprehensive survey of research efforts in the past couple of decades on keystroke dynamics biometrics. We review the literature in light of various feature extraction, feature matching and classification methods for keystroke dynamics. We also discuss recent trends in keystroke dynamics research, including its use in mobile environments, as a soft biometrics, and its fusion with other biometric modalities. We further address the evaluation of keystroke biometric systems, including traditional and new performance metrics, and list publicly available keystroke datasets for performance benchmarks to promote synergy in the research community.

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Introducing touchstroke: keystroke‐based authentication system for smartphones

Cited by 93 publications

References 30 publications

Performance Analysis for a Touch Dynamic Authentication System with Reduced Feature Set Using Neural Networks

Performance Analysis for a Touch Dynamic Authentication System with Reduced Feature Set Using Neural Networks

Authentication of Smartphone Users Using Behavioral Biometrics

A Survey on Keystroke Dynamics Biometrics: Approaches, Advances, and Evaluations

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