Automated generation of colluding apps for experimental research

Colluding apps bypass the security measures enforced by sandboxed operating systems such as Android. App collusion can be a real threat in cloud environments as well. Research in detecting and protecting against app collusion requires a variety of colluding apps for experimentation. Presently the number of (real or manually crafted) apps available to researchers is very limited. In this paper we propose a system called Application Collusion Engine (ACE) to automatically generate combinations of colluding and non-colluding Android apps to help researchers fairly evaluate different collusion detection and protection methods. Our initial implementation includes a variety of components that enable the system to create more than 5,000 different colluding and non-colluding app sets. ACE can be extended with more functional components to create even more colluding apps. To show the usefulness of our system, we have applied different risk evaluation and collusion detection methods to the created set of colluding apps

Advanced Security Analysis for Emergent Software Platforms

Emergent software ecosystems, boomed by the advent of smartphones and the Internet of Things (IoT) platforms, are perpetually sophisticated, deployed into highly dynamic environments, and facilitating interactions across heterogeneous domains. Accordingly, assessing the security thereof is a pressing need, yet requires high levels of scalability and reliability to handle the dynamism involved in such volatile ecosystems. This dissertation seeks to enhance conventional security detection methods to cope with the emergent features of contemporary software ecosystems. In particular, it analyzes the security of Android and IoT ecosystems by developing rigorous vulnerability detection methods. A critical aspect of this work is the focus on detecting vulnerable and unsafe interactions between applications that share common components and devices. Contributions of this work include novel insights and methods for: (1) detecting vulnerable interactions between Android applications that leverage dynamic loading features for concealing the interactions; (2) identifying unsafe interactions between smart home applications by considering physical and cyber channels; (3) detecting malicious IoT applications that are developed to target numerous IoT devices; (4) detecting insecure patterns of emergent security APIs that are reused from open-source software. In all of the four research thrusts, we present thorough security analysis and extensive evaluations based on real-world applications. Our results demonstrate that the proposed detection mechanisms can efficiently and effectively detect vulnerabilities in contemporary software platforms. Advisers: Hamid Bagheri and Qiben Ya