Contrasting permission patterns between clean and malicious android applications

The Android platform uses a permission system model to allow users and developers to regulate access to private information and system resources required by applications. Permissions have been proved to be useful for inferring behaviors and characteristics of an application. In this paper, a novel method to extract contrasting permission patterns for clean and malicious applications is proposed. Contrary to existing work, both required and used permissions were considered when discovering the patterns. We evaluated our methodology on a clean and a malware dataset, each comprising of 1227 applications. Our empirical results suggest that our permission patterns can capture key differences between clean and malicious applications, which can assist in characterizing these two types of applications.

Methods and technologies to promote information-centered knowledge construction

This alternative event for the 2013 iConference is a combination of lightning talks, a demonstration of an assessment technology for knowledge construction in complex domains, and a hands-on exercise in using the tools discussed. The unifying logic for this presentation is that meaningful learning often involves solving challenging and complex problems that allow for multiple solution approaches and a variety of acceptable solutions. While it is important to prepare students to solve such problems, it is difficult to determine the extent to which various interventions and programs are contributing to the development of appropriate problem-solving strategies and attitudes. Simply testing domain knowledge or the ability to solve simple, single-solution problems may not provide support for improving individual student ability or relevant programs and activities. A reliable and robust methodology for assessing the relevant knowledge constructions of students engaged in solving challenging problems is needed, and that is our focus.