Mouse dynamics based user recognition using deep learning

IF 0.3 Q4 COMPUTER SCIENCE, THEORY & METHODS Acta Universitatis Sapientiae Informatica Pub Date : 2020-07-01 DOI:10.2478/ausi-2020-0003
M. Antal, Norbert Fejér
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引用次数: 14

Abstract

Abstract Behavioural biometrics provides an extra layer of security for user authentication mechanisms. Among behavioural biometrics, mouse dynamics provides a non-intrusive layer of security. In this paper we propose a novel convolutional neural network for extracting the features from the time series of users’ mouse movements. The effect of two preprocessing methods on the performance of the proposed architecture were evaluated. Different training types of the model, namely transfer learning and training from scratch, were investigated. Results for both authentication and identification systems are reported. The Balabit public data set was used for performance evaluation, however for transfer learning we used the DFL data set. Comprehensive experimental evaluations suggest that our model performed better than other deep learning models. In addition, transfer learning contributed to the better performance of both identification and authentication systems.
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使用深度学习的基于用户识别的鼠标动态
行为生物识别技术为用户认证机制提供了一层额外的安全保障。在行为生物识别技术中,鼠标动态提供了一个非侵入性的安全层。本文提出了一种新颖的卷积神经网络,用于从用户鼠标移动的时间序列中提取特征。评估了两种预处理方法对所提体系性能的影响。研究了模型的不同训练类型,即迁移学习和从头开始训练。报告了认证和识别系统的结果。Balabit公共数据集用于性能评估,然而对于迁移学习,我们使用了DFL数据集。综合实验评估表明,我们的模型比其他深度学习模型表现得更好。此外,迁移学习有助于提高识别和认证系统的性能。
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来源期刊
Acta Universitatis Sapientiae Informatica
Acta Universitatis Sapientiae Informatica COMPUTER SCIENCE, THEORY & METHODS-
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