Android applications, or apps, provide useful features to end-users, but many apps also contain malicious behavior. Modern malware makes understanding such behavior challenging by behaving maliciously only under particular conditions. For example, a malware app may check whether it runs on a real device and not an emulator, in a particular country, and alongside a specific target app, such as a vulnerable banking app. To observe the malicious behavior, a security analyst must find out and emulate all these app-specific constraints. This paper presents FuzzDroid, a framework for automatically generating an Android execution environment where an app exposes its malicious behavior. The key idea is to combine an extensible set of static and dynamic analyses through a search-based algorithm that steers the app toward a configurable target location. On recent malware, the approach reaches the target location in 75% of the apps. In total, we reach 240 code locations within an average time of only one minute. To reach these code locations, FuzzDroid generates 106 different environments, too many for a human analyst to create manually.

Shake Them All is a popular "Wallpaper" application exceeding millions of downloads on Google Play. At installation, this application is given permission to (1) access the Internet (for updating wallpapers) and (2) use the device microphone (to change background following noise changes). With these permissions, the application could silently record user conversations and upload them remotely. To give more confidence about how Shake Them All actually processes what it records, it is necessary to build a precise analysis tool that tracks the flow of any sensitive data from its source point to any sink, especially if those are in different components. Since Android applications may leak private data carelessly or maliciously, we propose IccTA, a static taint analyzer to detect privacy leaks among components in Android applications. IccTA goes beyond state-of-the-art approaches by supporting inter- component detection. By propagating context information among components, IccTA improves the precision of the analysis. IccTA outperforms existing tools on two benchmarks for ICC-leak detectors: DroidBench and ICC-Bench. Moreover, our approach detects 534 ICC leaks in 108 apps from MalGenome and 2,395 ICC leaks in 337 apps in a set of 15,000 Google Play apps.

What is it that makes an app malicious? One important factor is that malicious apps treat sensitive data differently from benign apps. To capture such differences, we mined 2,866 benign Android applications for their data flow from sensitive sources, and compare these flows against those found in malicious apps. We find that (a) for every sensitive source, the data ends up in a small number of typical sinks; (b) these sinks differ considerably between benign and malicious apps; (c) these differences can be used to flag malicious apps due to their abnormal data flow; and (d) malicious apps can be identified by their abnormal data flow alone, without requiring known malware samples. In our evaluation, our MUDFLOW prototype correctly identified 86.4% of all novel malware, and 90.1% of novel malware leaking sensitive data.

Smartphones are nowadays used to store and process many kinds of privacy-sensitive data such as contacts, photos, and e-mails. Sensors provide access to the phone's physical location, and can record audio and video. While this is convenient for many applications, it also makes smartphones a worthwhile target for attackers providing malicious applications. Current approaches to runtime enforcement try to mitigate unauthorized leaks of confidential data. However, they are often capable of enforcing only a very limited set of policies, like preventing data leaks only within single components or monitoring access only to specific sensitive system resources. In this work, we present Droid Force, an approach for enforcing complex, data-centric, system-wide policies on Android applications. Droid Force allows users to specify fine-grained constraints on how and when which data may be processed on their phones, regardless of whether the malicious behavior is distributed over different colluding components or even applications. Policies can be dynamically exchanged at runtime and no modifications to the operating system nor root access to the phone are required. Droid Force works purely on the application level. It provides a centralized policy decision point as a dedicated Android application and it instruments a decentralized policy enforcement point into every target application. Analyzing and instrumenting an application takes in total less than a minute and secured applications exhibit no noticeable slowdown in practice.

Today's smartphones are a ubiquitous source of private and confidential data. At the same time, smartphone users are plagued by carelessly programmed apps that leak important data by accident, and by malicious apps that exploit their given privileges to copy such data intentionally. While existing static taint-analysis approaches have the potential of detecting such data leaks ahead of time, all approaches for Android use a number of coarse-grain approximations that can yield high numbers of missed leaks and false alarms.