A parameterized model to select discriminating features on keystroke dynamics authentication on smartphones

Lee, Hyungu; Hwang, Jung Yeon; Lee, Shincheol; Kim, Dong In; Lee, Sung-Hoon; Lee, John J.; Shin, Ji‐Sun

doi:10.1016/j.pmcj.2019.02.001

Cited by 16 publications

(11 citation statements)

References 17 publications

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“…Most recently, in 2019, another study concentrated on selecting features during keystroke dynamics authentications [12]. The motivation behind that study is similar to that behind ours.…”

Section: ) Comparative Experiments Of Keystroke Feature Subsetmentioning

confidence: 70%

“…Assuming that the arithmetic mean of the data set X containing n data is X , the formulas of the standard deviation std and the variation coefficient cv are as shown in the formula (11) and (12), respectively.…”

Section: ) Coefficient Of Variationmentioning

confidence: 99%

“…In the smartphone environment, keystroke dynamics data include the keypress/release time, touch size, coordinates, and movement data from motion sensors. We focus on the application of keystroke dynamics authentication because this study is follow-up research to an earlier study [12]. In addition, keystroke dynamics authentications require a high level of privacy protection of user data sets.…”

Section: Introductionmentioning

confidence: 99%

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A New Feature Scoring Method in Keystroke Dynamics-Based User Authentications

2020

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In recent years, computing devices have become widely distributed, and the accumulated data from these devices are growing rapidly, especially as they are increasingly equipped with various sensors and RF communication capabilities. Data science, including machine learning technology, has contributed to the better handling the large amounts of data and feature selection techniques have been a useful strategy. As data amounts continue to grow, scaling features will became crucial in data science. In this paper, we propose a novel filter-based feature-selection method in the context of keystroke dynamics authentication. In particular, we propose a new feature-scoring method and apply it to keystroke-dynamics-based authentications. We implement keystroke-dynamics-based authentications multi-factored with PIN-based authentications and collect data from actual users' testing experiments. Then, we apply our feature-selection method and compare the performance with that when using all of the features and existing feature-selection methods. Our experimental results show that the classification performance by the proposed method is superior to those of the other methods by up to 21.8%. Moreover, our method provides security to other users' data sets, as the method utilizes only mean values from imposter data. Our feature-selection method contributes to improving the quality of keystroke dynamics authentications without user privacy issues. More generally, our method can also be applied to other data-mining data sets, such as IoT sensor data sets.

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“…Most recently, in 2019, another study concentrated on selecting features during keystroke dynamics authentications [12]. The motivation behind that study is similar to that behind ours.…”

Section: ) Comparative Experiments Of Keystroke Feature Subsetmentioning

confidence: 70%

Section: ) Coefficient Of Variationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A New Feature Scoring Method in Keystroke Dynamics-Based User Authentications

2020

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“…Keystroke authentication is further divided into fixed text and free text studies. Lee et al [ 17 ] studied the dynamic features of user keystrokes and proposed a parametric model approach that can select the most distinguishing features for each user with a false rejection rate of 11%. However, keystroke behavior gradually decreases on most mobile devices since there is no virtual keyboard on lots of wearable devices [ 18 ].…”

Section: Related Workmentioning

confidence: 99%

Gait-Based Implicit Authentication Using Edge Computing and Deep Learning for Mobile Devices

Zeng

Zhang

Yang

et al. 2021

Sensors

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Implicit authentication mechanisms are expected to prevent security and privacy threats for mobile devices using behavior modeling. However, recently, researchers have demonstrated that the performance of behavioral biometrics is insufficiently accurate. Furthermore, the unique characteristics of mobile devices, such as limited storage and energy, make it subject to constrained capacity of data collection and processing. In this paper, we propose an implicit authentication architecture based on edge computing, coined Edge computing-based mobile Device Implicit Authentication (EDIA), which exploits edge-based gait biometric identification using a deep learning model to authenticate users. The gait data captured by a device’s accelerometer and gyroscope sensors is utilized as the input of our optimized model, which consists of a CNN and a LSTM in tandem. Especially, we deal with extracting the features of gait signal in a two-dimensional domain through converting the original signal into an image, and then input it into our network. In addition, to reduce computation overhead of mobile devices, the model for implicit authentication is generated on the cloud server, and the user authentication process also takes place on the edge devices. We evaluate the performance of EDIA under different scenarios where the results show that i) we achieve a true positive rate of 97.77% and also a 2% false positive rate; and ii) EDIA still reaches high accuracy with limited dataset size.

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“…An even larger list of sensors has been considered in [38], where a 6-digit PIN ('766420') has been collected for 100 times from each of 20 subjects using a Nexus 5X phone. Specifically, the acquired data comprise timing information, finger pressure and area, TC, accelerometer and linear accelerometer, rotation and game-rotation, gyroscope, and uncalibrated gyroscope.…”

Section: Personal Identification Numbersmentioning

confidence: 99%

Mobile keystroke dynamics for biometric recognition: An overview

2020

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In the relatively recent past, the analysis of keystroke dynamics for biometric recognition purposes has intrigued researchers, since practical evidences have shown differences in the typing behaviours of distinct subjects. This area of research has become even more appealing since the emergence and evolution of mobile smartphones, given their pervasiveness and intensive use in real‐life applications. In addition, unlike hard keyboards used with computers, mobile smartphones offer the possibility of exploiting embedded sensors to augment the information acquired when typing on their soft keyboards. This study discusses the state of the art of keystroke‐dynamics‐based automatic recognition system, exclusively when dealing with mobile devices, for both verification and identification purposes. In more details, the databases employed, the features selected, the methodologies implemented, and the performance achieved through the technological advances introduced in the last years, are here overviewed.

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A parameterized model to select discriminating features on keystroke dynamics authentication on smartphones

Cited by 16 publications

References 17 publications

A New Feature Scoring Method in Keystroke Dynamics-Based User Authentications

A New Feature Scoring Method in Keystroke Dynamics-Based User Authentications

Gait-Based Implicit Authentication Using Edge Computing and Deep Learning for Mobile Devices

Mobile keystroke dynamics for biometric recognition: An overview

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