Biblio
Lehigh University has set a goal to implement System Center Configuration Manager by the end of 2017. This project is being spearheaded by one of our Senior Computing Consultants who has been researching and trained in the Microsoft Virtualization stack. We will discuss our roadmaps, results from our proof-of-concept environments, and discussions in driving this project.
The high usability of smartphones and tablets is embraced by consumers as well as the corporate and public sector. However, especially in the non-consumer area the factor security plays a decisive role for the platform-selection process. All of the current companies within the mobile device sector added a wide range of security features to the initially consumer-oriented devices (Apple, Google, Microsoft), or have dealt with security as a core feature from the beginning (RIM, now Blackerry). One of the key security features for protecting data on the device or in device backups are encryption systems, which are available in the majority of current devices. However, even under the assumption that the systems are implemented correctly, there is a wide range of parameters, specific use cases, and weaknesses that need to be considered when deploying mobile devices in security-critical environments. As the second part in a series of papers (the first part was on iOS), this work analyzes the deployment of the Android platform and the usage of its encryption systems within a security-critical context. For this purpose, Android's different encryption systems are assessed and their susceptibility to different attacks is analyzed in detail. Based on these results a workflow is presented, which supports deployment of the Android platform and usage of its encryption systems within security-critical application scenarios.
This paper proposes and describes an active authentication model based on user profiles built from user-issued commands when interacting with GUI-based application. Previous behavioral models derived from user issued commands were limited to analyzing the user's interaction with the *Nix (Linux or Unix) command shell program. Human-computer interaction (HCI) research has explored the idea of building users profiles based on their behavioral patterns when interacting with such graphical interfaces. It did so by analyzing the user's keystroke and/or mouse dynamics. However, none had explored the idea of creating profiles by capturing users' usage characteristics when interacting with a specific application beyond how a user strikes the keyboard or moves the mouse across the screen. We obtain and utilize a dataset of user command streams collected from working with Microsoft (MS) Word to serve as a test bed. User profiles are first built using MS Word commands and identification takes place using machine learning algorithms. Best performance in terms of both accuracy and Area under the Curve (AUC) for Receiver Operating Characteristic (ROC) curve is reported using Random Forests (RF) and AdaBoost with random forests.