An Evaluation of One-Class and Two-Class Classification Algorithms for Keystroke Dynamics Authentication on Mobile Devices

Mobile devices such as smartphones have until now been protected by traditional authentication methods, including passwords or pattern locks. These authentication mechanisms are difficult to remember and are often disabled, leaving the device vulnerable if stolen. This paper investigates the possibility of unobtrusive, continuous authentication for smartphones based on biometric data collected using a touchscreen. The possibility of authenticating users on a smartphone was evaluated by conducting an experiment simulating real-world touch interaction. Touch data was collected from 30 participants during normal phone use. The touch features were analysed in terms of the information provided for authentication. It was found that features such as finger pressure, location of touch interaction and shape of the finger were important discriminators for authentication. The touch data was also analysed using two classification algorithms to measure the authentication accuracy. The results show that touch data is sufficiently distinct between users to be used in authentication without disrupting normal touch interaction. It is also shown that the raw touch data was more effective in authentication than the aggregated gesture data.

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Keystroke Dynamics Advances for Mobile Devices Using Deep Neural Network

Gate to Computer Science and Research - Recent Advances in User Authentication Using Keystroke Dynamics Biometrics ◽

10.15579/gcsr.vol2.ch4 ◽

2015 ◽

pp. 59-70 ◽

Cited By ~ 5

Author(s):

Yunbin Deng ◽

Yu Zhong

Keyword(s):

Neural Network ◽

Mobile Devices ◽

Deep Neural Network ◽

Keystroke Dynamics

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BioGames: a new paradigm and a behavioral biometrics collection tool for research purposes

Information and Computer Security ◽

10.1108/ics-12-2020-0196 ◽

2021 ◽

Vol ahead-of-print (ahead-of-print) ◽

Author(s):

Ioannis Stylios ◽

Spyros Kokolakis ◽

Andreas Skalkos ◽

Sotirios Chatzis

Keyword(s):

Mobile Devices ◽

Sensor Data ◽

New Paradigm ◽

Keystroke Dynamics ◽

Biometric Data ◽

Behavioral Biometrics ◽

Content Type ◽

General Data Protection Regulation ◽

Touch Gestures ◽

Data Separation

Purpose The purpose of this paper is to present a new paradigm, named BioGames, for the extraction of behavioral biometrics (BB) conveniently and entertainingly. To apply the BioGames paradigm, the authors developed a BB collection tool for mobile devices named BioGames App. The BioGames App collects keystroke dynamics, touch gestures, and motion modalities and is available on GitHub. Interested researchers and practitioners may use it to create their datasets for research purposes. Design/methodology/approach One major challenge for BB and continuous authentication (CA) research is the lack of actual BB datasets for research purposes. The compilation and refinement of an appropriate set of BB data constitute a challenge and an open problem. The issue is aggravated by the fact that most users are reluctant to participate in long demanding procedures entailed in the collection of research biometric data. As a result, they do not complete the data collection procedure, or they do not complete it correctly. Therefore, the authors propose a new paradigm and introduce a BB collection tool, which they call BioGames, for the extraction of biometric features in a convenient way. The BioGames paradigm proposes a methodology where users play games without participating in an experimental painstaking process. The BioGames App collects keystroke dynamics, touch gestures, and motion modalities. Findings The authors proposed a new paradigm for the collection of BB on mobile devices and created the BioGames application. The BioGames App is an Android application that collects BB data on mobile devices and sends them to a database. The database design allows multiple users to store their sensor data at any time. Thus, there is no concern about data separation and synchronization. BioGames App is General Data Protection Regulation (GDPR) compliant as it collects and processes only anonymous data. Originality/value The BioGames App is a publicly available tool that combines the keystroke dynamics, touch gestures, and motion modalities. In addition, it uses a methodology where users play games without participating in an experimental painstaking process.

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Performance Analysis of Keystroke Dynamics Using Classification Algorithms

2020 3rd International Conference on Information and Computer Technologies (ICICT) ◽

10.1109/icict50521.2020.00027 ◽

2020 ◽

Author(s):

Alaa Darabseh ◽

Doyel Pal

Keyword(s):

Performance Analysis ◽

Classification Algorithms ◽

Keystroke Dynamics

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Which Utterance Types Are Most Suitable to Detect Hypernasality Automatically?

Applied Sciences ◽

10.3390/app11198809 ◽

2021 ◽

Vol 11 (19) ◽

pp. 8809

Author(s):

Ignacio Moreno-Torres ◽

Andrés Lozano ◽

Enrique Nava ◽

Rosa Bermúdez-de-Alvear

Keyword(s):

Mobile Devices ◽

Selection Process ◽

Mobile App ◽

Classification Algorithms ◽

Healthy Controls ◽

Automatic Evaluation ◽

Acoustic Features ◽

Formant Frequency ◽

Clinical Recommendations ◽

Multiple Classifiers

Automatic tools to detect hypernasality have been traditionally designed to analyze sustained vowels exclusively. This is in sharp contrast with clinical recommendations, which consider it necessary to use a variety of utterance types (e.g., repeated syllables, sustained sounds, sentences, etc.) This study explores the feasibility of detecting hypernasality automatically based on speech samples other than sustained vowels. The participants were 39 patients and 39 healthy controls. Six types of utterances were used: counting 1-to-10 and repetition of syllable sequences, sustained consonants, sustained vowel, words and sentences. The recordings were obtained, with the help of a mobile app, from Spain, Chile and Ecuador. Multiple acoustic features were computed from each utterance (e.g., MFCC, formant frequency) After a selection process, the best 20 features served to train different classification algorithms. Accuracy was the highest with syllable sequences and also with some words and sentences. Accuracy increased slightly by training the classifiers with between two and three utterances. However, the best results were obtained by combining the results of multiple classifiers. We conclude that protocols for automatic evaluation of hypernasality should include a variety of utterance types. It seems feasible to detect hypernasality automatically with mobile devices.

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