Continuous User Authentication in Mobile Phone Browser Based on Gesture Characteristics

Luzbashev, Artem V.; Filippov, Alexander I.; Kogos, Konstantin

doi:10.1109/worlds4.2018.8611589

Cited by 4 publications

(3 citation statements)

References 9 publications

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“…Clustering variants are also used: k-means [38,39,29,40,41], dbscan [23], c-means [42]. Support Vector Machines (SVM) are also used, sometimes as a single [43] or best classifier in the experiment [29,44] and sometimes to support a claim that another method performs better [45]. Bayesian approaches (Naive Bayes [46,47] Naive Bernoulli Bayes [2], Bayes net [8,48,42]) are also used very often, with the argument that they are simple and fast to train and still have a surprisingly good performance.…”

Section: Resultsmentioning

confidence: 99%

User Behavior Analysis for Detecting Compromised User Accounts: A Review Paper

Jurišić,

Tomičić,

Grd

2023

Cybernetics and Information Technologies

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The rise of online transactions has led to a corresponding increase in online criminal activities. Account takeover attacks, in particular, are challenging to detect, and novel approaches utilize machine learning to identify compromised accounts. This paper aims to conduct a literature review on account takeover detection and user behavior analysis within the cybersecurity domain. By exploring these areas, the goal is to combat account takeovers and other fraudulent attempts effectively.

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Section: Resultsmentioning

confidence: 99%

User Behavior Analysis for Detecting Compromised User Accounts: A Review Paper

Jurišić,

Tomičić,

Grd

2023

Cybernetics and Information Technologies

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“…With various sensors built into smart mobile devices, such as touchscreens, accelerometers, and so forth, behavioral biometrics can be captured for continuous user authentication when people use or carry these devices. Luzbashev et al [16] proposed a method for smartphone user authentication via consecutive swipe gesture recognition, which depended essentially on the gesture trajectory and gesture dynamic generated from the touch screen. Dybczak et al [17] presented a smartphone continuous authentication system based on user hand movements utilizing inbuilt sensors such as the accelerometer and the gyroscope.…”

Section: Related Workmentioning

confidence: 99%

Continuous User Authentication on Multiple Smart Devices

Wang

Zhang

2023

Information

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Recent developments in the mobile and intelligence industry have led to an explosion in the use of multiple smart devices such as smartphones, tablets, smart bracelets, etc. To achieve lasting security after initial authentication, many studies have been conducted to apply user authentication through behavioral biometrics. However, few of them consider continuous user authentication on multiple smart devices. In this paper, we investigate user authentication from a new perspective—continuous authentication on multi-devices, that is, continuously authenticating users after both initial access to one device and transfer to other devices. In contrast to previous studies, we propose a continuous user authentication method that exploits behavioral biometric identification on multiple smart devices. In this study, we consider the sensor data captured by accelerometer and gyroscope sensors on both smartphones and tablets. Furthermore, multi-device behavioral biometric data are utilized as the input of our optimized neural network model, which combines a convolutional neural network (CNN) and a long short-term memory (LSTM) network. In particular, we construct two-dimensional domain images to characterize the underlying features of sensor signals between different devices and then input them into our network for classification. In order to strengthen the effectiveness and efficiency of authentication on multiple devices, we introduce an adaptive confidence-based strategy by taking historical user authentication results into account. This paper evaluates the performance of our multi-device continuous user authentication mechanism under different scenarios, and extensive empirical results demonstrate its feasibility and efficiency. Using the mechanism, we achieved mean accuracies of 99.8% and 99.2% for smartphones and tablets, respectively, in approximately 2.3 s, which shows that it authenticates users accurately and quickly.

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“…Human motion behavior characteristics can be applied in many fields, including gesture behavior [6], gait signal [7] [8] and keystroke dynamics [9] [10]. These behavior features can be used in identity authentication since they can represent the unique feature of each person.…”

Section: Behavior Feature Recognitionmentioning

confidence: 99%

Identity Authentication Based on Sensors of Smartphone and Neural Networks

Zhu¹,

Fan²,

Huang³

et al. 2022

JCC

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The smartphone has become an indispensable electric device for most people since it can assist us in finishing many tasks such as paying and reading. Therefore, the security of smartphones is the most crucial issue to illegal users who cannot access legal users' privacy information. This paper studies identity authentication using user action. This scheme does not rely on the password or biometric identification. It checks user identity just by user action features. We utilize sensors installed in smartphones and collect their data when the user waves the phone. We collect these data, process them and feed them into neural networks to realize identity recognition. We invited 13 participants and collected about 350 samples for each person. The sampling frequency is set at 200 Hz, and DenseNet is chosen as the neural network to validate system performance. The result shows that the neural network can effectively recognize user identity and achieve an authentication accuracy of 96.69 percent.

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Continuous User Authentication in Mobile Phone Browser Based on Gesture Characteristics

Cited by 4 publications

References 9 publications

User Behavior Analysis for Detecting Compromised User Accounts: A Review Paper

User Behavior Analysis for Detecting Compromised User Accounts: A Review Paper

Continuous User Authentication on Multiple Smart Devices

Identity Authentication Based on Sensors of Smartphone and Neural Networks

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