Mitigating Behavioral Variability for Mouse Dynamics: A Dimensionality-Reduction-Based Approach
Mitigating Behavioral Variability for Mouse Dynamics: A Dimensionality-Reduction-Based Approach
复制标题
减轻小鼠动力学的行为变异性：基于降维的方法
DOI：
10.1109/thms.2014.2302371
复制
发表时间：
2014-04-01
影响因子：
3.6
通讯作者：
Guan, Xiaohong
中科院分区：
文献类型：
--
作者：
Cai, Zhongmin;Shen, Chao;Guan, Xiaohong
鼠标动力学是根据用户的鼠标操作行为识别个人用户的过程。老鼠动力学分析技术不能提供可接受的准确度，可能是由于行为的可变性。本研究提出一种基于降维的方法来缓解鼠标动力学行为的变异性，提高基于鼠标动力学的连续认证的性能。对从每个鼠标行为数据会话中提取的原理图特征和运动技能特征的可变性进行了测量。提出了一个采用降维方法(多维尺度、拉普拉斯特征映射、等距特征映射和局部线性嵌入)的统一框架，通过从原始特征空间中获取优势特征来降低行为变异性。将分类技术(随机森林、支持向量机、神经网络和最近邻)应用到变换后的特征空间来执行认证任务。分析使用了28名参与者的840个半小时会议的数据。结果表明，对于足够长的序列，变换后的特征空间具有较小的变化性，相应的认证性能优于原始特征空间，在某些情况下，错误接受率和错误拒绝率分别提高了89.6%和77.4%。此外，对可变性和认证错误率与检测时间之间的关系的研究表明，随着检测时间的增加，可变性和认证错误率大大降低。就收集的数据而言，这种方法比最先进的方法表现得更好。这些发现表明，减少可变性可以改善鼠标动力学，因此它可能会增强当前的身份验证机制。
Mouse dynamics is the process of identifying individual users on the basis of their mouse operating behaviors. Mouse dynamics analysis techniques do not provide an acceptable level of accuracy, perhaps due to behavioral variability. This study presents a dimensionality-reduction-based approach to mitigate the behavioral variability of mouse dynamics and improve the performance of mouse-dynamics-based continuous authentication. Variability was measured over the schematic features and motor-skill features extracted from each mouse behavior data session. A unified framework of employing dimensionality reduction methods (Multidimensional Scaling, Laplacian Eigenmap, Isometric Feature Mapping, and Local Linear Embedding) was developed to reduce behavioral variability by obtaining predominant characteristics from the original feature space. Classification techniques (Random Forest, Support Vector Machine, Neural Network, and Nearest Neighbor) were applied to the transformed feature space to perform the authentication task. Analyses were conducted using data from 840 half-hour sessions of 28 participants. Results indicated that for sufficiently long sequences, the transformed feature spaces had much less variability and the corresponding authentication performance was better than the original feature space with improvements of the false-acceptance rate by 89.6% and of the false-rejection rate by 77.4% in some cases. Additionally, an investigation of the relationships between variability and authentication error rates and detection time indicated that the variability and authentication error rates reduce greatly with the increase of detection time. For the data collected, the approach fared better than the state-of-the-art approaches. These findings suggest that variability reduction could improve mouse dynamics, so it may enhance current authentication mechanisms.