Prof. Dr. Eric Bodden

Today’s smart phones are a ubiquitous source of private and confidential data. At the same time, smartphone users are plagued by malicious apps that exploit their given privileges to steal such sensitive data, or to track users without their consent or even the users noticing. Dynamic program analy- ses fail to discover such malicious activity because apps have learned to recognize the analyses as they execute.

In this work we present FlowDroid, a novel and highly precise taint analysis for Android applications. A precise model of Android’s lifecycle allows the analysis to prop- erly handle callbacks, while context, flow, field and object- sensitivity allows the analysis to track taints with a degree of precision unheard of from previous Android analyses.

We also propose DroidBench, an open test suite for evaluating the effectiveness and accuracy of taint-analysis tools specifically for Android apps. As we show through a set of experiments using SecuriBench Micro, DroidBench and a set of well-known Android test applications, our approach finds a very high fraction of data leaks while keeping the rate of false positives low. On DroidBench, our approach achieves 93% recall and 86% precision, greatly outperforming the commercial tools AppScan Source and Fortify SCA. 

Where can I find more information?

More information is available here.

Today’s smartphone users face a security dilemma: many apps they install operate on privacy-sensitive data, although they might originate from developers whose trustworthiness is hard to judge. Researchers have proposed more and more sophisticated static and dynamic analysis tools as an aid to assess the behavior of such applications. Those tools, however, are only as good as the privacy policies they are configured with. Policies typically refer to a list of sources of sensitive data as well as sinks which might leak data to untrusted observers. Sources and sinks are a moving target: new versions of the mobile operating system regularly introduce new methods, and security tools need to be re- configured to take them into account.

In this work we show that, at least for the case of Android, the API comprises hundreds of sources and sinks. We propose SuSi, a novel and fully automated machine-learning approach for identifying sources and sinks directly from the Android source code. On our training set, SuSi achieves a recall and precision of more than 92%. To provide more fine-grained information, SuSi further categorizes the sources (e.g., unique identifier, location information, etc.) and sinks (e.g., network, file, etc.), with an average precision and recall of about 89%. We also show that many current program analysis tools can be circumvented because they use hand-picked lists of source and sinks which are largely incomplete, hence allowing many potential data leaks to go unnoticed. 

Where can I find more information?

More information can be found here.