Keystroke Dynamics User Authentication Based on Gaussian Mixture Model and Deep Belief Nets

Deng, Yunbin; Zhong, Yu

doi:10.1155/2013/565183

Cited by 55 publications

(37 citation statements)

References 27 publications

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“…They improved EER by 0.9% and ZMFAR by 4.5% compared to the best results in [11]. Furthermore, the Gaussian mixture model (GMM) was applied [17] producing EER of 0.087. Despite the authors also reported additional even better results we omit them here as the testing procedure was different from the one described in [11] making the fair comparison impossible.…”

Section: Related Workmentioning

confidence: 97%

Anomaly detection approach to keystroke dynamics based user authentication

Ivannikova

David

Hämäläinen

2017

2017 IEEE Symposium on Computers and Communications (ISCC)

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Abstract-Keystroke dynamics is one of the authentication mechanisms which uses natural typing pattern of a user for identification. In this work, we introduced Dependence Clustering based approach to user authentication using keystroke dynamics. In addition, we applied a k-NN-based approach that demonstrated strong results. Most of the existing approaches use only genuine users data for training and validation. We designed a cross validation procedure with artificially generated impostor samples that improves the learning process yet allows fair comparison to previous works. We evaluated the methods using the CMU keystroke dynamics benchmark dataset. Both proposed approaches outperformed the previous state-of-the-art results for the CMU dataset for unsupervised learning.

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Section: Related Workmentioning

confidence: 97%

Anomaly detection approach to keystroke dynamics based user authentication

Ivannikova

David

Hämäläinen

2017

2017 IEEE Symposium on Computers and Communications (ISCC)

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“…Traditionally, these included keyboards, [11], [15], [19], and mouse [2], [18], [38]. Now, modern smartphones also provide an array of sensor information, which can be used similarly to construct user profiles based on touchscreen swipes [4], [14], gait analysis [12], [27], and other metrics.…”

Section: A Behavioral Biometricsmentioning

confidence: 99%

K-means++ vs. Behavioral Biometrics: One Loop to Rule Them All

Negi¹,

Sharma²,

Jain³

et al. 2018

Proceedings 2018 Network and Distributed System Security Symposium

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Abstract-Behavioral biometrics, a field that studies patterns in an individual's unique behavior, has been researched actively as a means of authentication for decades. Recently, it has even been adopted in many real world scenarios. In this paper, we study keystroke dynamics, the most researched of such behavioral biometrics, from the perspective of an adversary. We designed two adversarial agents with a standard accuracy convenience tradeoff: Targeted K-means++, which is an expensive, but extremely effective adversarial agent, and Indiscriminate K-means++, which is slightly less powerful, but adds no overhead cost to the attacker. With Targeted K-means++ we could compromise the security of 40-70% of users within ten tries. In contrast, with Indiscriminate K-means++, the security of 30-50% of users was compromised. Therefore, we conclude that while keystroke dynamics has potential, it is not ready for security critical applications yet. Future keystroke dynamics research should use such adversaries to benchmark the performance of the detection algorithms, and design better algorithms to foil these. Finally, we show that the K-means++ adversarial agent generalizes well to even other types of behavioral biometrics data by applying it on a dataset of touchscreen swipes.

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“…Both approaches have achieved the most competitive performances reported for the CMU benchmark dataset among more than 14 different systems. Both systems include training/modeling stages based exclusively on genuine data and other promising systems were discarded because they include impostor data during the training phase [23]. The approaches used in the experimental evaluation made in this paper are:…”

Section: Baseline Systemsmentioning

confidence: 99%

Towards Predicting Good Users for Biometric Recognition Based on Keystroke Dynamics

Morales

Fiérrez

Ortega‐Garcia

2015

Computer Vision - ECCV 2014 Workshops

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El acceso a la versión del editor puede requerir la suscripción del recurso Access to the published version may require subscription Abstract. This paper studies ways to detect good users for biometric recognition based on keystroke dynamics. Keystroke dynamics is an active research field for the biometric scientific community. Despite the great efforts made during the last decades, the performance of keystroke dynamics recognition systems is far from the performance achieved by traditional hard biometrics. This is very pronounced for some users, who generate many recognition errors even with the most sophisticate recognition algorithms. On the other hand, previous works have demonstrated that some other users behave particularly well even with the simplest recognition algorithms. Our purpose here is to study ways to distinguish such classes of users using only the genuine enrollment data. The experiments comprise a public database and two popular recognition algorithms. The results show the effectiveness of the Kullback-Leibler divergence as a quality measure to categorize users in comparison with other four statistical measures.

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Keystroke Dynamics User Authentication Based on Gaussian Mixture Model and Deep Belief Nets

Cited by 55 publications

References 27 publications

Anomaly detection approach to keystroke dynamics based user authentication

Anomaly detection approach to keystroke dynamics based user authentication

K-means++ vs. Behavioral Biometrics: One Loop to Rule Them All

Towards Predicting Good Users for Biometric Recognition Based on Keystroke Dynamics

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