2025 Available now Automated Privacy Analysis of the Android API Supervisor: David Noel Breuer Lucas Becker Android is the world's most used mobile operating system with many different versions, modified by Google, smartphone manufacturers, and the open source community. This results in countless Android versions on different smartphones. Each of these versions incorporates potential differences in its API, resulting in information that could be used for fingerprinting. In this Master's Thesis, you develop an automated solution to enumerate the Android API and analyze the outcome concerning its potential privacy leakage and fingerprintability. Prerequisites: Familiarity with android internals No fear of the unknown & scientific curiosity ;)
2025 Completed (May 2025) Surveillance or Service? Examining Privacy Issues in Grocery Retailer Applications Supervisor: David Noel Breuer Lucas Becker ...
2025 Completed (March 2025) Detecting Device Fingerprinting in Android Applications via API Hooking and Machine Learning Analysis Supervisor: David Noel Breuer Lucas Becker ...
2024 2024 IEEE Conference on Communications and Network Security (CNS) Conference A Data-Driven Evaluation of the Current Security State of Android Devices Ernst Leierzopf René Mayrhofer Michael Roland Wolfgang Studier Lawrence Dean Martin Seiffert Florentin Putz Lucas Becker Daniel Thomas BibTeX DOI: 10.1109/CNS62487.2024.10735682 Abstract Android’s fast-paced development cycles and the large number of devices from different manufacturers do not allow for an easy comparison between different devices’ security and privacy postures. Manufacturers each adapt and update their respective firmware images. Furthermore, images published on OEM websites do not necessarily match those installed in the field. Relevant software aspects do not remain static after initial device release, but need to be measured on real devices that receive these updates. There are various potential sources for collecting such attributes, including webscraping, crowdsourcing, and dedicated device farms. However, raw data alone is not helpful in making meaningful decisions on device security and privacy. We make a website available to access collected data. Our implementation focuses on reproducible requests and supports filtering by OEMs, devices, device models, and attributes. To improve usability, we further propose a security score grounded on the list of attributes. Based on input from Android experts, including a focus group and eight individuals, we have created a method that derives attribute weights from the importance of attributes for mitigating threats on the Android platform. We derive weights for general use cases and suggest possible examples for more specialized weights for groups of confidentiality/privacy-sensitive users and integrity-sensitive users. Since there is no one-size-fits-all setting for Android devices, our website provides the possibility to adapt all parameters of the calculated security score to individual needs.
2024 USENIX Conference on Offensive Technologies Proceedings of the 18th USENIX Conference on Offensive Technologies (WOOT'24) Conference Oh no, my RAN! breaking into an O-RAN 5G indoor base station Leon Janzen Lucas Becker Colin Wiesenäcker Matthias Hollick PDF BibTeX
2024 18th USENIX WOOT Conference on Offensive Technologies (WOOT 24) Conference SoK: On the Effectiveness of Control-Flow Integrity in Practice Lucas Becker Matthias Hollick Jiska Classen PDF BibTeX