Enhanced free-text keystroke continuous authentication based on dynamics of wrist motion

Li, Borui; Sun, Han Xu; Gao, Yang; Phoha, Vir V.; Jin, Zhanpeng

doi:10.1109/wifs.2017.8267642

Cited by 10 publications

(7 citation statements)

References 21 publications

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“…It is a behavioral biometrics which has the advantage of not requiring additional sensor than the keyboard. This biometric modality also allows continuous authentication through time [2,3].…”

Section: Introductionmentioning

confidence: 99%

Analysis of Keystroke Dynamics for the Generation of Synthetic Datasets

Migdal

Rosenberger

2018

2018 International Conference on Cyberworlds (CW)

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Biometrics is an emerging technology more and more present in our daily life. However, building biometric systems requires a large amount of data that may be difficult to collect. Collecting such sensitive data is also very time consuming and constrained, s.a. GDPR legislation. In the case of keystroke dynamics, existing databases have less than 200 users. For these reasons, we aim at generating a keystroke dynamics synthetic dataset. This paper presents the generation of keystroke data from known users as a first step towards the generation of synthetic datasets, and could also be used to impersonate users' identity.

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Section: Introductionmentioning

confidence: 99%

Analysis of Keystroke Dynamics for the Generation of Synthetic Datasets

Migdal

Rosenberger

2018

2018 International Conference on Cyberworlds (CW)

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“…The scheme was evaluated using Bayes Net (BN), Multilayer Perceptron (MLP), and RF classifiers on a dataset of 11 subjects and achieved a TAR 82.34%. Li et al [105] proposed a continuous authentication scheme based on free-text keystroke that exploited both keystroke latency patterns and wrist motion behaviors acquired by wrist-worn smartwatches. A Dynamic Trust Model (DTM) is developed to fuse two one-vs-all RF ensemble classifiers and achieved a TAR of 98.12% on 25 subjects.…”

Section: Footstepmentioning

confidence: 99%

A Survey of Human-Computer Interaction (HCI) & Natural Habits-based Behavioral Biometric Modalities for User Recognition Schemes

Gupta¹,

Maple²,

Crispo³

et al. 2022

Preprint

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The proliferation of Internet of Things (IoT) systems is having a profound impact across all aspects of life. Recognising and identifying particular users is central to delivering the personalised experience that citizens want to experience, and that organisations wish to deliver. This article presents a survey of human-computer interaction-based (HCI-based) and natural habits-based behavioral biometrics that can be acquired unobtrusively through smart devices or IoT sensors for user recognition purposes. Robust and usable user recognition is also a security requirement for emerging IoT ecosystems to protect them from adversaries. Typically, it can be specified as a fundamental building block for most types of human-to-things accountability principles and access-control methods. However, end-users are facing numerous security and usability challenges in using currently available knowledge- and token-based recognition (i.e., authentication and identification) schemes. To address the limitations of conventional recognition schemes, biometrics, naturally come as a first choice to supporting sophisticated user recognition solutions. We perform a comprehensive review of touch-stroke, swipe, touch signature, hand-movements, voice, gait and footstep behavioral biometrics modalities. This survey analyzes the recent state-of-the-art research of these behavioral biometrics with a goal to identify their attributes and features for generating unique identification signatures. Finally, we present security, privacy, and usability evaluations that can strengthen the designing of robust and usable user recognition schemes for IoT applications.

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“…Morales et al [16] used the scoring normalization technique to effectively improve the authentication accuracy of the KD authentication system. Li et al [17] presented authentication approach by incorporating the dynamics of both free-text keystroke latency features and statistical wrist motion patterns extracted from the wrist worn smartwatches, and dynamic trust model (DTM) was developed to fuse two one-vs-all Random Forest Ensemble Classifiers (RFECs). e result was that an impostor or intruder was detected within no more than one sentence (average 56 keystrokes) with the FRR of 1.82% and the FAR of 1.94%.…”

Section: Kdmentioning

confidence: 99%

User Authentication Method Based on MKL for Keystroke and Mouse Behavioral Feature Fusion

Wang

Zheng

et al. 2020

Security and Communication Networks

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In order to improve the recognition rate of users with single behavioral feature and prevent impostors from restricting an input device to avoid detection, a dual-index user authentication method based on Multiple Kernel Learning (MKL) for keystroke and mouse behavioral feature fusion was proposed in this paper. Due to the heterogeneity between the keystroke features and the mouse features, we argue that each type of features is mapped to a suitable kernel and the weights of each kernel are obtained through computing and then summed to obtain a compound kernel that implements the multifeature fusion. The dataset used in this paper was collected under complete uncontrolled condition from some volunteers by using our data collection program. The experimental results show that the proposed method can obtain the best recognition accuracy of 89.6%. Compared to the traditional methods of single feature, the dual-index method can get more stable and effective authentication. Therefore, the proposed method in this paper fully demonstrates the reliability of dual-index user authentication.

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Enhanced free-text keystroke continuous authentication based on dynamics of wrist motion

Cited by 10 publications

References 21 publications

Analysis of Keystroke Dynamics for the Generation of Synthetic Datasets

Analysis of Keystroke Dynamics for the Generation of Synthetic Datasets

A Survey of Human-Computer Interaction (HCI) & Natural Habits-based Behavioral Biometric Modalities for User Recognition Schemes

User Authentication Method Based on MKL for Keystroke and Mouse Behavioral Feature Fusion

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