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.
翻译:静态认证方法,如密码,随着技术和攻击策略的进步而变得越来越弱。 持续认证被作为一种解决方案提出来,让访问账户的用户仍然受到监测,以便不断核实用户不是可访问用户证书的冒牌人。 鼠标动态是用户鼠标运动的行为,它是一种生物鉴别技术,显示了持续认证计划的巨大希望。 文章以我们以前出版的40个用户数据集为基础,利用3个机器学习和深层学习算法,评估了我们40个用户的数据集。 考虑了两种评估设想:二元分类用于用户认证,顶级演艺人是一个1维共变神经网络,最高平均测试精确度为前10个用户的85.73%。多级分类还使用一个人工神经网络进行审查,其精度达到我们所看到的该数据集中任何分类员最高精度的92.48%。