SBE TWC: Small: Collaborative: Pocket Security - Smartphone Cybercrime in the Wild

世界上大多数互联网接入都是通过智能手机和平板电脑等移动设备进行的。虽然这些设备很方便，但它们也使跨越物理世界和网络空间的犯罪活动成为可能。例如，一个偷了智能手机的小偷可以接触到一个人？S的敏感电子邮件，或者在火车上使用银行应用的人可能会向监视她的人泄露账号。这项研究将研究人们如何、何时、在哪里使用智能手机，以及这些使用模式与成为网络犯罪受害者的可能性之间的关系。这项研究是更好地科学理解围绕智能手机使用的物理世界如何促成网络犯罪的第一步。疲惫的用户在浏览不安全的网站时可能不那么谨慎，或者分心的用户可能会错过检测到病毒的关键弹出窗口。一旦识别了这些不安全的行为模式，就可以开发新的技术、工具和培训来帮助防止智能手机用户成为网络犯罪的受害者。这项研究扩展了移动设备领域的现有受害理论，移动设备领域的犯罪活动和受害都发生在网上，但可能会受到线下环境的影响。这项研究从160名志愿者的智能手机中收集传感器数据，如GPS位置、呼叫频率和应用程序使用情况。智能手机传感器数据与问卷调查、美国人口普查数据和谷歌街景的社区状况数据相结合。这项研究还提供了源于行为和社会因素的智能手机安全威胁的基线，并应用了社会科学研究的新方法，使用移动传感器数据来不引人注目地观察受试者的日常活动。最后，这项研究增加了关于基于传感器的活动和上下文推断的基本限制的知识体系，提供了一个独特的智能手机传感器数据语料库，可供科学界免费使用，以及一套用于收集和分析数据的开源工具。

Most of the world's internet access occurs through mobile devices such as smart phones and tablets. While these devices are convenient, they also enable crimes that intersect the physical world and cyberspace. For example, a thief who steals a smartphone can gain access to a person?s sensitive email, or someone using a banking app on the train may reveal account numbers to someone looking over her shoulder. This research will study how, when, and where people use smartphones and the relationship between these usage patterns and the likelihood of being a victim of cybercrime. This research is the first step to a better scientific understanding how the physical world surrounding smartphone use enables cybercrime. Tired users may be less cautious in browsing to unsafe websites, or distracted users may miss a critical pop-up that a virus has been detected. Once these unsafe patterns of behavior are identified, new techniques, tools, and training can be developed to help prevent smartphone users from becoming victims of cybercrime.This research expands existing theories of victimization in the domain of mobile devices, where both the criminal activity and the victimization occur online but may be affected by the offline environment. This research collects sensor data from the smartphones of 160 volunteers, such as GPS location, call frequency, and app usage. The smartphone sensor data is combined with questionnaires, demographic data from the U.S. Census, and neighborhood condition data from Google Street view. This research also provides a baseline of smartphone security threats stemming from behavioral and social factors, and applies new methods for social science research using mobile sensor data to unobtrusively observe the daily activities of subjects. Finally, this research adds to the body of knowledge on the fundamental limitations of sensor-based activity and context inferences, provides a unique corpus of smartphone sensor data that is freely available to the scientific community, and a set of open source tools for collecting and analyzing the data.