An Implicit Identity Authentication System Considering Changes of Gesture Based on Keystroke Behaviors

Wu, Jiyun; Chen, Zhide

doi:10.1155/2015/470274

Cited by 28 publications

(24 citation statements)

References 24 publications

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“…to build the model, samples from both legitimate and illegitimate subjects are used. This is also the case for the work reported in papers (Buriro et al 2017;Ho 2013;Wu and Chen 2015). However, in real-life, as mobile devices are very much personal devices, illegitimate subject samples may not always be available.…”

Section: Related Workmentioning

confidence: 74%

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Strengthen user authentication on mobile devices by using user’s touch dynamics pattern

Teh

Zhang

Tan

et al. 2019

J Ambient Intell Human Comput

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Mobile devices, particularly the touch screen mobile devices, are increasingly used to store and access private and sensitive data or services, and this has led to an increased demand for more secure and usable security services, one of which is user authentication. Currently, mobile device authentication services mainly use a knowledge-based method, e.g. a PIN-based authentication method, and, in some cases, a fingerprint-based authentication method is also supported. The knowledgebased method is vulnerable to impersonation attacks, while the fingerprint-based method can be unreliable sometimes. To overcome these limitations and to make the authentication service more secure and reliable for touch screen mobile device users, we have investigated the use of touch dynamics biometrics as a mobile device authentication solution by designing, implementing and evaluating a touch dynamics authentication method. This paper describes the design, implementation, and evaluation of this method, the acquisition of raw touch dynamics data, the use of the raw data to obtain touch dynamics features, and the training of the features to build an authentication model for user identity verification. The evaluation results show that by integrating the touch dynamics authentication method into the PIN-based authentication method, the protection levels against impersonation attacks is greatly enhanced. For example, if a PIN is compromised, the success rate of an impersonation attempt is drastically reduced from 100% (if only a 4-digit PIN is used) to 9.9% (if both the PIN and the touch dynamics are used).

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Section: Related Workmentioning

confidence: 74%

“…By far, the best accuracy performance was reported by (Wu and Chen 2015). The authors achieved an EER value of 0.56%.…”

Section: Related Workmentioning

confidence: 98%

Strengthen user authentication on mobile devices by using user’s touch dynamics pattern

Teh

Zhang

Tan

et al. 2019

J Ambient Intell Human Comput

View full text Add to dashboard Cite

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“…In addition, studies using both orientation and accelerometers have been researched. Xu et al [27] guessed the enter keys and showed 88.7% accuracy which is higher than the former study, and Wu and Chen [14] showed 0.556% EER using these two features and time, pressure, and size features. Table 1 shows the mobile keystroke dynamics studies.…”

Section: Related Workmentioning

confidence: 79%

Understanding Keystroke Dynamics for Smartphone Users Authentication and Keystroke Dynamics on Smartphones Built-In Motion Sensors

Lee

Hwang

Kim

et al. 2018

Security and Communication Networks

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Personal Identification Numbers (PINs) and pattern drawing have been used as common authentication methods especially on smartphones. Such methods, however, are very vulnerable to the shoulder surfing attack. Thus, keystroke dynamics that authenticate legitimate users based on their typing manner have been studied for years. However, many of the studies have focused on PC keyboard keystrokes. More studies on mobile and smartphones keystroke dynamics are warranted; as smartphones make progress in both hardware and software, features from smartphones have been diversified. In this paper, using various features including keystroke data such as time interval and motion data such as accelerometers and rotation values, we evaluate features with motion data and without motion data. We also compare 5 formulas for motion data, respectively. We also demonstrate that opposite gender match between a legitimate user and impostors has influence on authenticating by our experiment results.

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“…Many researches make use of Android applications to collect activity data and use back‐end classifiers to identify activity classes. Activity recognition from sensors data is generally treated as a classification problem, with different authors proposing different machine learning approaches to its solution .…”

Section: Related Workmentioning

confidence: 99%

Human continuous activity recognition based on energy-efficient schemes considering cloud security technology

Chen

Castiglione

et al. 2016

Security Comm. Networks

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Smartphone is broadly applicable to the human activity recognition (HAR) mobile devices. However, energy consumption becomes a big obstacle to such mobile devices of real‐time monitoring. In order to solve this problem, this paper presents a method of activity recognition based on energy‐efficient schemes. In terms of data acquisition and processing, energy‐efficient schemes adopt the best sample rate and extract the most effective feature combinations in accordance with the different activities, so as not to increase energy consumption; while in terms of recognition algorithm, we adopt the improved structure of multi‐class support vector machine, combine it with the probability of activity occurrence, so as to reduce the time complexity of recognition. This method can minimize energy consumption greatly under the premise of maintaining higher recognition accuracy. Moreover, this paper adopts mobile cloud security technology to reduce potential risk of the smartphone's data transmission and processing. We present our work with an experimental study, and our experiments show that the accuracy of activity recognition based on energy‐efficient schemes we proposed is up to 90.6%. In addition, this method will save 51.0% energy than that when sample rate and extracted features are, respectively, fixed at 100Hz and combined features. Copyright © 2016 John Wiley & Sons, Ltd.

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An Implicit Identity Authentication System Considering Changes of Gesture Based on Keystroke Behaviors

Cited by 28 publications

References 24 publications

Strengthen user authentication on mobile devices by using user’s touch dynamics pattern

Strengthen user authentication on mobile devices by using user’s touch dynamics pattern

Understanding Keystroke Dynamics for Smartphone Users Authentication and Keystroke Dynamics on Smartphones Built-In Motion Sensors

Human continuous activity recognition based on energy-efficient schemes considering cloud security technology

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