Hijacked Smart Devices - Methodical Foundations for Autonomous Theft Awareness based on Activity Recognition and Novelty Detection
Hijacked Smart Devices - Methodical Foundations for Autonomous Theft Awareness based on Activity Recognition and Novelty Detection
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DOI：
10.5220/0006594901310142
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发表时间：
2018
影响因子：
5.2
通讯作者：
Martin Jänicke;V. Schmidt;B. Sick;Sven Tomforde;P. Lukowicz
中科院分区：
文献类型：
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作者：
Martin Jänicke;V. Schmidt;B. Sick;Sven Tomforde;P. Lukowicz
智能手机等个人设备在日常生活中的使用越来越多。通常，在这些设备上执行活动识别以估计当前用户状态并根据用户需求触发自动操作。在本文中，我们专注于提高此类系统在检测盗窃方面的自我意识：我们为设备配备了对其自己的用户进行建模的功能，并能够在意外的其他人携带设备时向合法用户发出警报。我们在一个有14人使用诺基亚N97的案例研究中收集了24小时的数据，并对一个活动识别系统进行了培训。在此基础上，我们开发并研究了一种自主新颖性检测系统，该系统持续检查观察到的用户行为是否与初始模型相对应，如果不符合，则发出警报。我们的评估表明，该方法是非常成功的，对于训练过的一组人，成功的盗窃率超过了85%。与最先进技术的对比实验支持了我们方法的强大实用性。
Personal devices such as smart phones are increasingly utilised in everyday life. Frequently, Activity Recognition is performed on these devices to estimate the current user status and trigger automated actions according to the user’s needs. In this article, we focus on improving the self-awareness of such systems in terms of detecting theft: We equip devices with the capabilities to model their own user and to, e.g., alarm the legal user if an unexpected other person is carrying the device. We gathered 24hours of data in a case study with 14 persons using a Nokia N97 and trained an activity recognition system. Based on it, we developed and investigated an autonomous novelty detection system that continuously checks if the observed user behavior corresponds to the initial model, and that gives an alarm if not. Our evaluations show that the presented method is highly successful with a successful theft detection rate of over 85% for the trained set of persons. Comparison experiments with state of the art techniques support the strong practicality of our approach.