Multi-Modality Mobile Datasets for Behavioral Biometrics Research: Data/Toolset paper
Multi-Modality Mobile Datasets for Behavioral Biometrics Research: Data/Toolset paper
复制标题
用于行为生物识别研究的多模态移动数据集：数据/工具集论文
DOI：
10.1145/3577923.3583637
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发表时间：
2023
期刊：
影响因子：
--
通讯作者：
Schuckers, Stephanie
中科院分区：
文献类型：
--
作者：
Ray-Dowling, Aratrika;Wahab, Ahmed Anu;Hou, Daqing;Schuckers, Stephanie
当今移动的设备的普遍存在需要保护存储在其中的应用和用户信息。然而，现有的一次性入口点身份验证机制和增强的安全机制（如多因素身份验证（MFA））容易受到广泛的攻击。此外，MFA也会给用户体验带来摩擦。因此，所需要的是连续认证，其一旦通过入口点认证，将通过确认设备的合法所有者并锁定检测到的冒名顶替者活动来在连续的基础上保护移动的设备。因此，需要对移动的安全的动态方法进行更多的研究，例如基于行为生物特征的连续认证，这是具有成本效益的和被动的，因为用于认证用户的数据是从手机的传感器记录的。然而，目前，没有太多的移动的身份验证数据集进行基准测试研究。在这项工作中，我们共享两个新的移动的数据集（克拉克森大学（CU）移动的数据集I和II）组成的多模态行为生物特征数据分别来自49和39个用户（共88个用户）。我们的每一个数据集都由各种形式组成，如滑动、滑动、加速度、陀螺仪和模式跟踪笔划。当用户以真实用户和冒名顶替用户的身份填写注册表时，这些模式被收集。为了展示数据集的有用性，我们已经对从数据集中选择的各个模态以及同时可用的模态的融合进行了初步实验。
The ubiquity of mobile devices nowadays necessitates securing the apps and user information stored therein. However, existing one-time entry-point authentication mechanisms and enhanced security mechanisms such as Multi-Factor Authentication (MFA) are prone to a wide vector of attacks. Furthermore, MFA also introduces friction to the user experience. Therefore, what is needed is continuous authentication that once passing the entry-point authentication, will protect the mobile devices on a continuous basis by confirming the legitimate owner of the device and locking out detected impostor activities. Hence, more research is needed on the dynamic methods of mobile security such as behavioral biometrics-based continuous authentication, which is cost-effective and passive as the data utilized to authenticate users are logged from the phone's sensors. However, currently, there are not many mobile authentication datasets to perform benchmarking research. In this work, we share two novel mobile datasets (Clarkson University (CU) Mobile datasets I and II) consisting of multi-modality behavioral biometrics data from 49 and 39 users respectively (88 users in total). Each of our datasets consists of modalities such as swipes, keystrokes, acceleration, gyroscope, and pattern-tracing strokes. These modalities are collected when users are filling out a registration form in sitting both as genuine and impostor users. To exhibit the usefulness of the datasets, we have performed initial experiments on selected individual modalities from the datasets as well as the fusion of simultaneously available modalities.
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DOI：
10.1109/btas.2016.7791155
发表时间：
2016-09
期刊：
2016 IEEE 8th International Conference on Biometrics Theory, Applications and Systems (BTAS)
影响因子：
--
作者：
U. Mahbub;Sayantan Sarkar;Vishal M. Patel;R. Chellappa
通讯作者：
U. Mahbub;Sayantan Sarkar;Vishal M. Patel;R. Chellappa
使用公共数据集评估多模式移动行为生物识别
影响因子：
5.6
作者：
Ray-Dowling, Aratrika;Hou, Daqing;Schuckers, Stephanie;Barbir, Abbie
通讯作者：
Barbir, Abbie
通过击键动力学保护台式计算机和移动电话上的帐户恢复机制
DOI：
10.1007/s42979-022-01245-3
发表时间：
2022
期刊：
SN Computer Science
影响因子：
--
作者：
Wahab, Ahmed Anu;Hou, Daqing;Schuckers, Stephanie;Barbir, Abbie
通讯作者：
Barbir, Abbie
影响因子：
8
作者：
Stragapede, Giuseppe;Vera-Rodriguez, Ruben;Morales, Aythami
通讯作者：
Morales, Aythami