SEALANT: A detection and visualization tool for inter-app security vulnerabilities in androic
Authors:
- 1 October 2017
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- p. 883-888
Abstract
Android's flexible communication model allows interactions among third-party apps, but it also leads to inter-app security vulnerabilities. Specifically, malicious apps can eavesdrop on interactions between other apps or exploit the functionality of those apps, which can expose a user's sensitive information to attackers. While the state-of-the-art tools have focused on detecting inter-app vulnerabilities in Android, they neither accurately analyze realistically large numbers of apps nor effectively deliver the identified issues to users. This paper presents SEALANT, a novel tool that combines static analysis and visualization techniques that, together, enable accurate identification of inter-app vulnerabilities as well as their systematic visualization. SEALANT statically analyzes architectural information of a given set of apps, infers vulnerable communication channels where inter-app attacks can be launched, and visualizes the identified information in a compositional representation. SEALANT has been demonstrated to accurately identify inter-app vulnerabilities from hundreds of real-world Android apps and to effectively deliver the identified information to users. (Demo Video: https://youtu.be/E4lLQonOdUw)Keywords
This publication has 13 references indexed in Scilit:
- Practical, Formal Synthesis and Automatic Enforcement of Security Policies for Android2016
- IccTA: Detecting Inter-Component Privacy Leaks in Android Apps2015
- Composite Constant Propagation: Application to Android Inter-Component Communication Analysis2015
- COVERT: Compositional Analysis of Android Inter-App Permission LeakageIEEE Transactions on Software Engineering, 2015
- Amandroid2014
- Android taint flow analysis for app sets2014
- ViVA: a visualization and analysis tool for distributed event-based systems2014
- A Machine-learning Approach for Classifying and Categorizing Android Sources and Sinks2014
- CHEX2012
- Analyzing inter-application communication in Android2011