Context Awareness means that the device becomes aware of its environment like the current location, time, and temperature. Apps can use context awareness to provide services to users in areas like health care and navigation.
MUSES utilizes context data to identify threats of corporate data security by analyzing user behavior. The aim of the project is to improve corporate data security by reducing the risks, introduced by user behavior (more information: why-muses). MUSES faces several security challenges like users accessing corporate networks from personal devices (BYOD), the personal perception of security, and users mixing professional and private device usage. MUSES achieves its target by relaying on context data that are analyzed to identify threats to corporate data security, by means of corporate policies. One of the biggest challenge is, that it’s hard to draw a line between professional and private device usage, especially when users mix such activities proposing security risks to corporate data.
HITeC addresses this issue within its research activities under the name of Situation Prediction. The goal of the Situation Prediction is to automatically differentiate the device usage in professional and private on the fly. To achieve this goal, HITeC developed an Android app that uses supervised machine learning. Therefore, in an ongoing study, participants are asked about their intended usage, whenever they unlock their device (Screenshot 1). If they are not sure at this point, they also have the choice to be asked again, when they turn off their device. As the usage might change at any time, the participants are able to update their current device usage in the notification bar (Screenshot 2). This choice is related to the context data that we collect with the Android app, like clicks, scrolls, location, used applications and connection properties. We hope to gain a deeper understanding of device usage in order to enhance the privacy of the user within the MUSES app, by e.g. protecting personal data.