Continuous authentication of smartphone users by fusing typing, swiping, and phone movement patterns

Kumar, Rajesh; Phoha, Vir V.; Serwadda, Abdul

doi:10.1109/btas.2016.7791164

Cited by 70 publications

(47 citation statements)

References 12 publications

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“…Because the verification happens at quite frequent intervals and users can be locked out after certain successive rejects. To implement the continuous part of the system, generally, a sliding window-based mechanism is used [2,6,19]. The authentication decisions are given either based on the patterns captured in the current window or in the last few windows.…”

Section: Continuous Authenticationmentioning

confidence: 99%

“…The data was collected from 28 volunteers in a completely realistic and unconstrained environment for four to seven days. We replicated the data preprocessing and feature extraction steps exactly as proposed Kumar et al in [2], and used the selected features as advised for creating genuine and impostor samples. The average number of samples per user was 55.35 and 55.82 for training and testing sessions respectively.…”

Section: Swiping and Phone Movement Patternsmentioning

confidence: 99%

“…We will refer to this dataset as Swiping and Phone Movement Patterns (SP M P ) in the rest of the paper. For this dataset, we replicated only one of the experiments i.e., a feature-level fusion of swiping and phone movements with a single-template framework as presented by Kumar et al [2].…”

Section: Swiping and Phone Movement Patternsmentioning

confidence: 99%

“…For each dataset, we replicated the experimental setup advised by respective authors in their papers [19], [6], [2]. For the PABG dataset, the authors studied five classifiers: Bayes Network, Logistic Regression, Multilayer Perceptrons, Random Forests, and Support Vector Machines [19].…”

Section: Choice Of Classifiersmentioning

confidence: 99%

“…The multi-class classifiers (M CC) have been widely studied for building behavioral biometric based continuous authentication systems [1][2][3][4]. The disadvantages of using M CC is that they require samples from both genuine and impostor classes to determine respective decision boundaries.…”

Section: Introductionmentioning

confidence: 99%

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Continuous authentication using one-class classifiers and their fusion

Kumar

Kundu

Phoha

2018

2018 IEEE 4th International Conference on Identity, Security, and Behavior Analysis (ISBA)

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While developing continuous authentication systems (CAS), we generally assume that samples from both genuine and impostor classes are readily available. However, the assumption may not be true in certain circumstances.Therefore, we explore the possibility of implementing CAS using only genuine samples. Specifically, we investigate the usefulness of four one-class classifiers OCC (elliptic envelope, isolation forest, local outliers factor, and one-class support vector machines) and their fusion. The performance of these classifiers was evaluated on four distinct behavioral biometric datasets, and compared with eight multi-class classifiers (M CC). The results demonstrate that if we have sufficient training data from the genuine user the OCC, and their fusion can closely match the performance of the majority of M CC. Our findings encourage the research community to use OCC in order to build CAS as they do not require knowledge of impostor class during the enrollment process.

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Section: Continuous Authenticationmentioning

confidence: 99%

Section: Swiping and Phone Movement Patternsmentioning

confidence: 99%

Section: Swiping and Phone Movement Patternsmentioning

confidence: 99%