Mobile user identification through authentication using keystroke dynamics and accelerometer biometrics

Corpus, Kyle R.; Gonzales, Ralph Joseph D.L.; Morada, Alvin Scott; Vea, Larry A.

doi:10.1145/2897073.2897111

Cited by 22 publications

(9 citation statements)

References 1 publication

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“…We user accuracy as evaluation index of device identification effect. The calculation method of accuracy is shown in formula (6). TP is the number of successful identification and correct, FP is the number of successful identification but false judgment.…”

Section: Experimental Results and Analysismentioning

confidence: 99%

“…Yuechi et al [5] proposed a mobile phone identifier called Weighted Support Vector Machine with Weighted Majority Voting (WSVM-WMV) for a closed-set mobile phone identification task. Kyle et al [6] discussed the use of keystroke dynamics, a form of behavioral biometrics that deals with the measure of how a person types, and the utilization of accelerometer biometrics as a form of behavioral biometric that measures how a person holds his mobile device. But their study is limited to mobile devices using Android OS.…”

Section: Introductionmentioning

confidence: 99%

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Zero-Permission Mobile Device Identification Based on the Similarity of Browser Fingerprints

Kang¹,

Chen²,

Feng³

et al. 2017

dtcse

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Abstract. Mobile device identification techniques can be applied to security authentication, access control and other fields. It is used to uniquely indentify a mobile device through explicit identifiers, e.g., UUID, Android ID, or according to the difference of mobile device acceleration sensor. However, these methods collect features data requiring specified conditions or relying on sensitive permissions. To settle these issues, we propose a zero-permission mobile device identification algorithm based on similarity of browser fingerprints. The algorithm firstly obtains 11 features that are acquired without requesting any permission, e.g., User-agent, screen resolution. These 11 features are combined to form a browser fingerprint of mobile device. Then, defining the different similarity calculation methods of features, and matching fingerprints to identify device according to similarity. Finally, Experiments verify the feasibility and accuracy of the algorithm, the results show that the algorithm can effectively identify device.

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Section: Experimental Results and Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Zero-Permission Mobile Device Identification Based on the Similarity of Browser Fingerprints

Kang¹,

Chen²,

Feng³

et al. 2017

dtcse

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“…Corpus et al studied the combination of KD and accelerometer biometrics features for mobile authentication. 24 The data was gathered from 30 users by asking them to type eight times a password consisting of 16 alpha-numeric characters with at least one (1) special-character password, and to record a password eight times using the accelerometer biometrics. The combination of both traits produced a FAR of 7% and a FRR of 40% using Neural Network classifier.…”

Section: Multimodal Schemesmentioning

confidence: 99%

“…Several multimodal proposals have been published in the literature that combine mobile KD with other existing biometric modalities, such as 2D face, behavioral profiling, linguistic profiling, and voice . Saevanee et al proposed an integration of multimodal biometric traits using matching‐level fusion to enhance the accuracy performance of mobile authentication.…”

Section: Background and Related Workmentioning

confidence: 99%

“…By applying SVM algorithm with score fusion, an EER of 1.11% was obtained. Corpus et al studied the combination of KD and accelerometer biometrics features for mobile authentication . The data was gathered from 30 users by asking them to type eight times a password consisting of 16 alpha‐numeric characters with at least one (1) special‐character password, and to record a password eight times using the accelerometer biometrics.…”

Section: Background and Related Workmentioning

confidence: 99%

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Multimodal mobile keystroke dynamics biometrics combining fixed and variable passwords

Alshanketi

Traoré

Awad

2019

Security and Privacy

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Recent works have demonstrated the possibility to craft successful statistical attacks against keystroke dynamic biometric password. Those attacks leverage the possibility to capture several keystroke dynamics samples for a given password string, and then extract and use their distributional properties to craft the attack. These approaches are by design more likely to be successful when launched against fixed passwords, as several samples of the passwords can be captured through successive login sessions. Although the dynamics obtained from specific keys or key sequences for consecutive passwords slightly vary, by definition the distributional properties remain fairly stable for the same user. One way to thwart such that attack to use a variable password, also know as one‐time password (OTP). However, the fact that the keystroke dynamic OTP is different from one session to the other, makes it extremely difficult to reconstruct a valid biometric profile for a user. Modeling accurate keystroke dynamic OTP is challenging, due to the underlying variability and the sparse amount of information involved. We tackle the aformentioned challenge by presenting, in this paper, by presenting a multimodal approach tat combines fixed and variable keystroke dynamic biometric passwords. We investigate two different fusion models and evaluate our approach using a data set involving 100 different users, yielding encouraging performance results in terms of accuracy and resistance against statistical attacks.

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A Short Survey: Behavioral Authentication Using Mobile Sensors

Ali

Ponnusamy

Sangodiah

et al. 2020

International Conference on Mobile Computing and Sustainable Informatics

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Mobile user identification through authentication using keystroke dynamics and accelerometer biometrics

Cited by 22 publications

References 1 publication

Zero-Permission Mobile Device Identification Based on the Similarity of Browser Fingerprints

Zero-Permission Mobile Device Identification Based on the Similarity of Browser Fingerprints

Multimodal mobile keystroke dynamics biometrics combining fixed and variable passwords

A Short Survey: Behavioral Authentication Using Mobile Sensors

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