Touch-interaction behavior for continuous user authentication on smartphones

Shen, Chao; Zhang, Yong; Cai, Zhongmin; Yu, Tianwen; Guan, Xiaohong

doi:10.1109/icb.2015.7139046

Cited by 19 publications

(7 citation statements)

References 16 publications

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“…(Draffin et al, 2014) 13 6000 touch events Yes NN 86 * (Meng et al, 2013) 20 10 min touch events Yes NN 2.92 (Xu et al, 2014) 32 5 min touch events No SVM <10 (Feng et al, 2013) 40 14-53 Characters No DT 1 (Y. 50 120 touch events Yes NN 2.46 (Shen et al, 2015) 51 800 touch events Yes SVM <8 (Serwadda et al, 2013) 190 80 touch events No PM ~13.8 (Gascon et al, 2014) 315 160 Characters No SVM 1^, 8 # * Accuracy; ^F AR; # FRR…”

Section: Dynamic Modementioning

confidence: 99%

A survey on touch dynamics authentication in mobile devices

Teh

Zhang

Teoh

et al. 2016

Computers & Security

130

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There have been research activities in the area of keystroke dynamics biometrics on physical keyboards (desktop computers or conventional mobile phones) undertaken in the past three decades. However, in terms of touch dynamics biometrics on virtual keyboards (modern touchscreen mobile devices), there has been little published work. Particularly, there is a lack of an extensive survey and evaluation of the methodologies adopted in the area. Owing to the widespread use of touchscreen mobile devices, it is necessary for us to examine the techniques and their effectiveness in the domain of touch dynamics biometrics. The aim of this paper is to provide some insights and comparative analysis of the current state-of-the-art in the topic area, including data acquisition protocols, feature data representations, decision making techniques, as well as experimental settings and evaluations. With such a survey, we can gain a better understanding of the current state-of-the-art, thus identifying challenging issues and knowledge gaps for further research.

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Section: Dynamic Modementioning

confidence: 99%

A survey on touch dynamics authentication in mobile devices

Teh

Zhang

Teoh

et al. 2016

Computers & Security

130

View full text Add to dashboard Cite

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“…Mobile device platform information, specially touch, is also used to some extent, e.g. [164], [178] or [44]. However, the use of body-related data, biosignals in particular, is experiencing a significant growth and in the last 3 years, in which 6 approaches have been proposed [194]…”

Section: Manuscript Submitted To Acmmentioning

confidence: 99%

Leveraging User-related Internet of Things for Continuous Authentication

2019

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Among all Internet of Things (IoT) devices, a subset of them are related to users. Leveraging these user-related IoT elements, it is possible to ensure the identity of the user for a period of time, thus avoiding impersonation. This need is known as Continuous Authentication (CA). Since 2009, a plethora of IoT-based CA academic research and industrial contributions have been proposed. We offer a comprehensive overview of 58 research papers regarding the main components of such a CA system. The status of the industry is studied as well, covering 32 market contributions, research projects, and related standards. Lessons learned, challenges, and open issues to foster further research in this area are finally presented.

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“…This may allow for some useful counteraction, such as raising an alarm, blocking malicious content, or simply switching to higher protection thresholds and more verbose logs. For examples and surveys of applications, see [11][12][13][14][15][16][17][18].…”

Section: Definition and Frameworkmentioning

confidence: 99%

“…Applications of keyed learning fall within the scope of exploratory adversarial learning [2]. This context is generally appropriate for anomaly detection, which comprises several application domains, including intrusion detection [3,5,6,14,25,33,34], attack and malware analysis [7,16,[35][36][37], defacement response [8,17,38,39], Web promotional infection detection [40], and biometric and continuous user authentication [11,18].…”

Section: Applicationsmentioning

confidence: 99%

Keyed learning: An adversarial learning framework—formalization, challenges, and anomaly detection applications

Bergadano

2019

ETRI Journal

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We propose a general framework for keyed learning, where a secret key is used as an additional input of an adversarial learning system. We also define models and formal challenges for an adversary who knows the learning algorithm and its input data but has no access to the key value. This adversarial learning framework is subsequently applied to a more specific context of anomaly detection, where the secret key finds additional practical uses and guides the entire learning and alarm‐generating procedure.

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Touch-interaction behavior for continuous user authentication on smartphones

Cited by 19 publications

References 16 publications

A survey on touch dynamics authentication in mobile devices

A survey on touch dynamics authentication in mobile devices

Leveraging User-related Internet of Things for Continuous Authentication

Keyed learning: An adversarial learning framework—formalization, challenges, and anomaly detection applications

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