Social Behaviometrics for Personalized Devices in the Internet of Things Era
Social Behaviometrics for Personalized Devices in the Internet of Things Era
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DOI：
10.1109/access.2017.2719706
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发表时间：
2017-01-01
期刊：
影响因子：
3.9
通讯作者：
Schuckers, Stephanie
中科院分区：
文献类型：
--
作者：
Anjomshoa, Fazel;Aloqaily, Moayad;Schuckers, Stephanie
随着智能移动的设备到物联网（IoT）应用的集成变得越来越普遍，移动终端使用、与其他设备的交互以及用户的移动模式携带关于拥有这些设备的用户的日常生活的大量信息。如果在一段时间内观察到这组丰富的数据，则可以用于有效地验证用户。在之前的工作中，通过指纹和虹膜等生物识别特性对个性化电子设备上的用户进行验证已成功用于提高访问的安全性。然而，随着社交网络与物联网基础设施的集成及其在智能手持设备上的普及，基于社交网络上的行为的识别正在成为一个新的概念。在本文中，我们提出了一个智能附加的智能设备，使用户的持续验证。在实验中，我们使用内置传感器的数据和移动的设备上五种不同社交网络应用程序的使用统计数据。所收集的特征集随时间聚合并使用机器学习技术进行分析。我们表明，当智能设备配备连续验证智能时，可以以低于10%的错误拒绝概率验证用户，并且用户可以在90%的时间内不中断地使用设备进行生物特征认证。在异常行为模式的情况下，该系统可以验证真正的用户与高达97%的成功率使用聚合的行为模式上五个不同的社交网络应用程序。
As the integration of smart mobile devices to the Internet of Things (IoT) applications is becoming widespread, mobile device usage, interactions with other devices, and mobility patterns of users carry significant amount of information about the daily routines of the users who are in possession of these devices. This rich set of data, if observed over a time period, can be used to effectively verify a user. In previous works, verification of users on personalized electronic devices via biometric properties, such as fingerprint and iris, has been successfully employed to increase the security of access. However, with the integration of social networks with the IoT infrastructure and their popularity on smart handheld devices, identification based on behavior over social networks is emerging as a novel concept. In this paper, we propose an intelligent add-on for the smart devices to enable continuous verification of users. In the experiments, we use data from built-in sensors and usage statistics of five different social networking applications on mobile devices. The collected feature set is aggregated over time and analyzed using machine learning techniques. We show that when smart devices are equipped with continuous verification intelligence, it is possible to verify users with less than 10% false rejection probabilities, and the users can keep using the devices with no interruption for biometric authentication 90% of the time. In the case of anomalous behavioral patterns, the proposed system can verify genuine users with up to 97% success ratio using an aggregated behavior pattern on five different social network applications.