Biometrics is used to authenticate an individual based on physiological or
behavioral traits. Mouse dynamics is an example of a behavioral biometric that
can be used to perform continuous authentication as protection against security
breaches. Recent research on mouse dynamics has shown promising results in
identifying users; however, it has not yet reached an acceptable level of
accuracy. In this paper, an empirical evaluation of different classification
techniques is conducted on a mouse dynamics dataset, the Balabit Mouse
Challenge dataset. User identification is carried out using three mouse
actions: mouse move, point and click, and drag and drop. Verification and
authentication methods are conducted using three machine-learning classifiers:
the Decision Tree classifier, the K-Nearest Neighbors classifier, and the
Random Forest classifier. The results show that the three classifiers can
distinguish between a genuine user and an impostor with a relatively high
degree of accuracy. In the verification mode, all the classifiers achieve a
perfect accuracy of 100%. In authentication mode, all three classifiers
achieved the highest accuracy (ACC) and Area Under Curve (AUC) from scenario B
using the point and click action data: (Decision Tree ACC:87.6%, AUC:90.3%),
(K-Nearest Neighbors ACC:99.3%, AUC:99.9%), and (Random Forest ACC:89.9%,
AUC:92.5%).

该研究使用生物特征识别对个人进行身份认证，其中鼠标动力学是一种行为生物特征，可以用于进行连续身份验证以防止安全漏洞。该论文通过对鼠标动力学数据集 Balabit Mouse Challenge 数据集进行了不同分类技术的实证评估，使用鼠标移动、点选和拖放三种动作进行用户识别，并使用决策树分类器、K 最近邻分类器和随机森林分类器进行验证和认证。结果显示，这三种分类器可以以相对较高的准确率区分真实用户和冒名顶替者。在验证模式下，所有分类器的准确率达到 100%。在认证模式下，三种分类器在使用点选操作数据的情景 B 中实现了最高准确率（决策树准确率：87.6%，AUC：90.3%），（K 最近邻准确率：99.3%，AUC：99.9%）和（随机森林准确率：89.9%，AUC：92.5%）。

使用鼠标点击数据分析的连续认证

Continuous Authentication Using Mouse Clickstream Data Analysis

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.

本文采用三种机器学习和深度学习算法，评估了 40 名用户使用鼠标动态作为生物特征的连续身份验证方案，其中包括二元分类器和多元分类器，并获得了在数据集上最高的准确度。