Evaluating multi-modal mobile behavioral biometrics using public datasets
Evaluating multi-modal mobile behavioral biometrics using public datasets
复制标题
使用公共数据集评估多模式移动行为生物识别
DOI：
10.1016/j.cose.2022.102868
复制
发表时间：
2022
影响因子：
5.6
通讯作者：
Barbir, Abbie
中科院分区：
文献类型：
--
作者：
Ray-Dowling, Aratrika;Hou, Daqing;Schuckers, Stephanie;Barbir, Abbie
基于行为生物特征的连续用户认证在保护手机安全的同时，也是对传统安全机制的补充。然而，现有的技术状态执行持续的认证来评估深度学习模型，但缺乏对数据的不同特征集的检查。因此，我们评估了基于加速度、陀螺仪(角速度)和从两个公共移动数据集HMOG(手移动、方向和抓取)(Sitováet al.，(2015)a数据集等)进行用户身份验证的性能。(2015))和BB-MAS(来自相同用户的多设备和多活动行为数据)(Belman等人，(2019)数据集等人)。(2019))提取不同的特征集，观察认证性能的变化。我们评估了这两种独立模式及其融合的性能。由于滑动数据是间歇性的，但运动事件数据是连续的，我们评估了滑动与发生在滑动内的运动事件的融合与滑动之外的运动事件的融合。此外，我们提取了Frank等人的S(2012年)Touchalytics的特写。(2012)，但有三个不同的功能集(中位数、HMOG(Sitováet al.(2015))，以及沈等人(Shen.et al.(2017)，其中沈氏的特征表现最好。更具体地说，我们利用二进制支持向量机(Support VectorMachine，简称为支持向量机)对单个通道进行分数级融合。此外，我们使用Nandakumar的基于似然比的分数融合(Nandakumar等人)来评估多个模式的融合。(2007))通过利用单类和二进制支持向量机。对于HMOG和BB-MAS，当使用单类支持向量机时，融合三种模式的最佳EER(等错误率)分别为8.8%和0.9%。另一方面，在二元支持向量机的情况下，最佳的EER分别为1.5%和0.2%。在基于滑动的实验中，我们观察了BB-MAS与HMOG相比的更好的性能，我们检查了两个数据集的滑动轨迹的差异，发现BB-MAS的滑动长度比HMOG更长，这就解释了实验中的性能差异。
Behavioral biometric-based continuous user authentication is promising for securing mobile phones while complementing traditional security mechanisms. However, the existing state of art perform continuous authentication to evaluate deep learning models, but lacks examining different feature sets over the data. Therefore, we evaluate the performance of user authentication based on acceleration, gyroscope (angular velocity), and swipe data from two public mobile datasets, HMOG (Hand-Movement, Orientation, and Grasp) (Sitová et al., (2015) dataset et al. (2015)) and BB-MAS (Behavioral Biometrics Multi-device and multi-Activity data from Same users) (Belman et al., (2019) dataset et al. (2019)) extracted with different feature sets to observe the variation in authentication performance. We evaluate the performances of both individual modalities and their fusion. Since the swipe data is intermittent but the motion event data continuous, we evaluate fusion of swipes with motion events that occur within the swipes versus fusion of motion events outside of swipes. Moreover, we extract Frank et al.’s (2012) Touchalytics features Frank et al. (2012) on the swipe data but three different feature sets (median, HMOG (Sitová et al. (2015)), and Shen’s (Shen et al. (2017))) on the motion event data, among which the Shen’s features were shown to perform the best. More specifically, we perform score-level fusion for a single modality utilizing binary SVMs (Support Vector Machine). Furthermore, we evaluate the fusion of multiple modalities using Nandakumar’s likelihood ratio-based score fusion (Nandakumar et al. (2007)) by utilizing both one-class and binary SVMs. The best EERs (Equal Error Rates) of fusing all three modalities when using the one-class SVMs are 8.8% and 0.9% for HMOG and BB-MAS respectively. On the other hand, the best EERs in the case of binary SVMs are 1.5% and 0.2% respectively. Observing the better performances of BB-MAS compared to HMOG in swipe-based experiments, we examine the difference of swipe trajectory between the two datasets and find that BB-MAS has longer swipes than HMOG which would explain the performance difference in the experiments.