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(searched for: doi:10.13176/11.764)
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Attaullah Buriro, , Artsiom Yautsiukhin, Bruno Crispo
Journal of Signal Processing Systems, Volume 93, pp 989-1006; https://doi.org/10.1007/s11265-021-01654-2

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, Chan Mee Yee, Adnan Bin Amanat Ali
Communications in Computer and Information Science pp 182-194; https://doi.org/10.1007/978-981-15-2693-0_13

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Published: 9 January 2019
Security and Privacy, Volume 2; https://doi.org/10.1002/spy2.48

Abstract:
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.
Wireless Communications and Mobile Computing, Volume 2018, pp 1-17; https://doi.org/10.1155/2018/7107295

Abstract:
Smart mobile devices are playing a more and more important role in our daily life. Cancelable biometrics is a promising mechanism to provide authentication to mobile devices and protect biometric templates by applying a noninvertible transformation to raw biometric data. However, the negative effect of nonlinear distortion will usually degrade the matching performance significantly, which is a nontrivial factor when designing a cancelable template. Moreover, the attacks via record multiplicity (ARM) present a threat to the existing cancelable biometrics, which is still a challenging open issue. To address these problems, in this paper, we propose a new cancelable fingerprint template which can not only mitigate the negative effect of nonlinear distortion by combining multiple feature sets, but also defeat the ARM attack through a proposed feature decorrelation algorithm. Our work is a new contribution to the design of cancelable biometrics with a concrete method against the ARM attack. Experimental results on public databases and security analysis show the validity of the proposed cancelable template.
Tempestt Neal, Damon L. Woodard
2017 IEEE International Joint Conference on Biometrics (IJCB) pp 71-79; https://doi.org/10.1109/btas.2017.8272684

Abstract:
Because passwords and personal identification numbers are easily forgotten, stolen, or reused on multiple accounts, the current norm for mobile device security is quickly becoming inefficient and inconvenient. Thus, manufacturers have worked to make physiological biometrics accessible to mobile device owners as improved security measures. While behavioral biometrics has yet to receive commercial attention, researchers have continued to consider these approaches as well. However, studies of interactive data are limited, and efforts which are aimed at improving the performance of such techniques remain relevant. Thus, this paper provides a performance analysis of application, Bluetooth, and Wi-Fi data collected from 189 subjects on a mobile device for user verification. Results indicate that user authentication can be achieved with up to 91% accuracy, demonstrating the effectiveness of associative classification as a feature extraction technique.
Tempestt J. Neal, Damon L. Woodard
2017 IEEE International Joint Conference on Biometrics (IJCB) pp 62-70; https://doi.org/10.1109/btas.2017.8272683

Abstract:
While mobile devices are no longer a new technology, using the data generated from the use of these devices for security purposes has just recently been explored. Current methods, such as passwords, are quickly becoming antiquated, lacking the robustness, accuracy, and convenience desired to serve as reliable security measures. Since, researchers have resorted to alternative techniques, such as measurements obtained from keyboard interactions and movement, and behavioral interactions, such as application usage. However, practical implementations require further evaluation of circumvention. Thus, this work thoroughly analyzes various threats against mobile devices which use mobile device usage data as behavioral biometrics for authentication. Experimental results indicate that an outsider with a certain level of knowledge regarding the behavior of the device's owner poses a great security threat. Possible countermeasures to prevent such attacks are also provided.
Chuan-Chin Teo, Han-Foon Neo
Proceedings of the 9th International Conference on E-Education, E-Business, E-Management and E-Learning - IC4E '18 pp 1-5; https://doi.org/10.1145/3093293.3093296

Abstract:
In any commercial applications, users need a secure model to protect their transactions. The behavioral biometric such as voice and signature is dynamic, naturally involves cognitive experience and controlled by human beings. This could be seen when user changes their signature or voice given different situations. As a result, users would be more convinced to use behavioral biometric for authentication against fingerprint, which is physiological and static. Fingerprint biometric stemmed from a number of predicaments with prove of stolen, artificial and cut-off finger cases. The dilemma to use either behavioral or physiological biometric delves in as the accuracy of behavioral biometric is not as reliable as physiological biometric. The physiological biometric such as fingerprint and iris recognition are unique and cannot be mimicked by others easily. In addressing this gap, an authentication framework combines a unique physiological fingerprint and permutated sequence, known as behavioral fingerprint is proposed in this paper. Behavioral fingerprint authentication serves as a firewall to block or delay unauthorized access to a security system if user's fingerprint was lost or compromised.
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