Machine and Deep Learning Applications to Mouse Dynamics for Continuous User Authentication

Machine Learning and Knowledge Extraction

Nyle Siddiqui¹  Mounika Vanamala¹  Naeem Seliya¹  Rushit Dave¹ 

[1] Department of Computer Science, University of Wisconsin—Eau Claire, Eau Claire, WI 54701, USA;

关键词: deep learning;    machine learning;    mouse dynamics;    continuous user authentication;    multi-class classification;   

DOI  :  10.3390/make4020023

来源: DOAJ

【 摘 要 】

Static authentication methods, like passwords, grow increasingly weak with advancements in technology and attack strategies. Continuous authentication has been proposed as a solution, in which users who have gained access to an account are still monitored in order to continuously verify that the user is not an imposter who had access to the user credentials. Mouse dynamics is the behavior of a user’s mouse movements and is a biometric that has shown great promise for continuous authentication schemes. This article builds upon our previous published work by evaluating our dataset of 40 users using three machine learning and three deep learning algorithms. Two evaluation scenarios are considered: binary classifiers are used for user authentication, with the top performer being a 1-dimensional convolutional neural network (1D-CNN) with a peak average test accuracy of 85.73% across the top-10 users. Multi-class classification is also examined using an artificial neural network (ANN) which reaches an astounding peak accuracy of 92.48%, the highest accuracy we have seen for any classifier on this dataset.

【 授权许可】

Unknown