E
Eric Bodden
Researcher at University of Paderborn
Publications - 219
Citations - 8255
Eric Bodden is an academic researcher from University of Paderborn. The author has contributed to research in topics: Computer science & Android (operating system). The author has an hindex of 36, co-authored 200 publications receiving 7093 citations. Previous affiliations of Eric Bodden include Technische Universität Darmstadt & Fraunhofer Society.
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Proceedings ArticleDOI
FlowDroid: precise context, flow, field, object-sensitive and lifecycle-aware taint analysis for Android apps
Steven Arzt,Siegfried Rasthofer,Christian Fritz,Eric Bodden,Alexandre Bartel,Jacques Klein,Yves Le Traon,Damien Octeau,Patrick McDaniel +8 more
TL;DR: FlowDroid is presented, a novel and highly precise static taint analysis for Android applications that successfully finds leaks in a subset of 500 apps from Google Play and about 1,000 malware apps from the VirusShare project.
Proceedings ArticleDOI
IccTA: detecting inter-component privacy leaks in Android apps
Li Li,Alexandre Bartel,Tegawendé F. Bissyandé,Jacques Klein,Yves Le Traon,Steven Arzt,Siegfried Rasthofer,Eric Bodden,Damien Octeau,Patrick McDaniel +9 more
TL;DR: IccTA, a static taint analyzer to detect privacy leaks among components in Android applications goes beyond state-of-the-art approaches by supporting inter- component detection and propagating context information among components, which improves the precision of the analysis.
Proceedings Article
Effective inter-component communication mapping in Android with Epicc: an essential step towards holistic security analysis
Damien Octeau,Patrick McDaniel,Somesh Jha,Alexandre Bartel,Eric Bodden,Jacques Klein,Yves Le Traon +6 more
TL;DR: This paper reduces the discovery of inter-component communication in smartphones to an instance of the Interprocedural Distributive Environment (IDE) problem, and develops a sound static analysis technique targeted to the Android platform that finds ICC vulnerabilities with far fewer false positives than the next best tool.
The Soot framework for Java program analysis: a retrospective
TL;DR: relevant features of Soot are described, its development process is summarized, and useful features for future program analysis frameworks are discussed.
Proceedings ArticleDOI
A Machine-learning Approach for Classifying and Categorizing Android Sources and Sinks
TL;DR: SUSI, a novel machine-learning guided approach for identifying sources and sinks directly from the code of any Android API, is proposed and shown that SUSI can reliably classify sources and sink even in new, previously unseen Android versions and components like Google Glass or the Chromecast API.