The current security mechanisms for Android apps, both static and dynamic analysis approaches, are insufficient for detection and prevention of the increasingly dynamic and sophisticated security attacks.
The absence of explicit architectural constructs in mainstream programming languages has prevented software developers from achieving the many benefits of architecture-based development. To address this issue, Java 9 has introduced the Java Platform Module System (JPMS), resulting in the first instance of modules with rich software architectural interfaces added to a mainstream programming language. JPMS aims to support the encapsulation, security, and maintainability of Java applications and the JDK.
Multi-criteria test-suite minimization aims to remove redundant test cases from a test suite based on some criteria such as code coverage, while trying to optimally maintain the capability of the reduced suite based on other criteria such as fault-detection effectiveness. Existing techniques addressing this problem with integer linear programming claim to produce optimal solutions. However, the multi-criteria test-suite minimization problem is inherently nonlinear, due to the fact that test cases are often dependent on each other in terms of test-case criteria.
Android is the dominant mobile platform with 85% market share, as of the first quarter of 2017. At the same time, the number and sophistication of malicious Android apps are increasin.
Although a wide variety of approaches identify vulnerabilities in Android apps, none attempt to determine exploitability of those vulnerabilities. Exploitability can aid in reducing false positives of vulnerability analysis, and can help engineers triage bugs. Specifically, one of the main attack vectors of Android apps is their inter-component communication (ICC) interface, where apps may receive messages called Intents.
We present a comprehensive review of the existing approaches for Android security analysis. The review is carried out to achieve the following objectives:
The number of malicious Android apps is increasing rapidly. Android malware can damage or alter other files or settings, install additional applications, etc. To determine such behaviors, a security analyst can significantly benefit from identifying the family to which an Android malware belongs, rather than only detecting if an app is malicious. Techniques for detecting Android malware, and determining their families, lack the ability to handle certain obfuscations that aim to thwart detection.
TrimDroid is a novel combinatorial approach for generating GUI system tests for Android apps.
TrimDroid is comprised of four major components: Model Extraction, Dependency Extraction, Sequence Generation, and Test-Case Generation. Together, these components produce a significantly smaller number of test cases than exhaustive combinatorial technique, yet achieve a comparable coverage.
COVERT is a tool for compositional verification of Android inter-application vulnerabilities. It automatically identifies vulnerabilities that occur due to the interaction of apps comprising a system. Subsequently, it determines whether it is safe for a bundle of apps, requiring certain permissions and potentially interacting with each other, to be installed together.