论文标题
用于连续用户身份验证的鼠标动力学的机器和深度学习应用
Machine and Deep Learning Applications to Mouse Dynamics for Continuous User Authentication
论文作者
论文摘要
随着技术和攻击策略的进步，静态身份验证方法（如密码）越来越虚弱。已经提出了连续的身份验证作为解决方案，在该解决方案中，仍在监视获得帐户访问权限的用户，以便不断验证用户不是能够访问用户凭证的冒名顶替者。鼠标动力学是用户鼠标运动的行为，是一种生物特征，它对连续身份验证方案显示出巨大的希望。本文通过使用三种机器学习和深度学习算法评估我们的40位用户的数据集来建立我们以前发表的工作。考虑了两种评估方案：二进制分类器用于用户身份验证，最佳表现者是一维卷积神经网络，在前10名用户中，峰值平均测试精度为85.73％。还使用人工神经网络对多类分类进行了检查，该神经网络达到了我们在该数据集中看到的任何分类器的惊人峰准确性的92.48％。
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 users 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 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 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 which reaches an astounding peak accuracy of 92.48% the highest accuracy we have seen for any classifier on this dataset.
