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

世界上大部分的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，但它们也使犯罪活动跨越了现实世界和网络空间。例如，一个偷了智能手机的小偷可以接近一个人吗？美国的敏感电子邮件，或者在火车上使用银行应用程序的人可能会向她身后的人透露账户号码。这项研究将研究人们使用智能手机的方式、时间和地点，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导致网络犯罪的第一步。疲惫的用户可能在浏览不安全的网站时不那么谨慎，或者分心的用户可能会错过检测到病毒的关键弹出窗口。一旦识别出这些不安全的行为模式，就可以开发新的技术、工具和培训，以帮助防止智能手机用户成为网络犯罪的受害者。本研究在移动设备领域扩展了现有的受害理论，在移动设备领域，犯罪活动和受害都发生在网上，但可能受到离线环境的影响。该研究收集了160名志愿者智能手机上的传感器数据，如GPS位置、通话频率、应用程序使用情况等。智能手机传感器数据与问卷调查、美国人口普查的人口统计数据以及谷歌街景的社区状况数据相结合。本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，并应用了使用移动传感器数据进行社会科学研究的新方法，以不引人注目地观察受试者的日常活动。最后，本研究增加了关于基于传感器的活动和上下文推断的基本局限性的知识体系，提供了一个独特的智能手机传感器数据语料库，可以免费提供给科学界，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.