Ammar Askar (Georgia Institute of Technology), Fabian Fleischer (Georgia Institute of Technology), Christopher Kruegel (University of California, Santa Barbara), Giovanni Vigna (University of California, Santa Barbara), Taesoo Kim (Georgia Institute of Technology)

Intents are the primary message-passing mechanism on Android, used for both communication between intra-app and inter-app components. Intents go across the trust boundary of applications and can break the security isolation between them. Due to their shared API with intra-app communication, apps may unintentionally expose functionality leading to important security bugs. MALintent is an open-source fuzzing framework that uses novel coverage instrumentation techniques and customizable bug oracles to find security issues in Android Intent handlers. MALintent is the first Intent fuzzer that applies greybox fuzzing on compiled closed-source Android applications. We demonstrate techniques widely compatible with many versions of Android and our bug oracles were able to find several crashes, vulnerabilities with privacy implications, and memory-safety issues in the top-downloaded Android applications on the Google Play store.

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EAGLEYE: Exposing Hidden Web Interfaces in IoT Devices via...

Hangtian Liu (Information Engineering University), Lei Zheng (Institute for Network Sciences and Cyberspace (INSC), Tsinghua University), Shuitao Gan (Laboratory for Advanced Computing and Intelligence Engineering), Chao Zhang (Institute for Network Sciences and Cyberspace (INSC), Tsinghua University), Zicong Gao (Information Engineering University), Hongqi Zhang (Henan Key Laboratory of Information Security), Yishun Zeng (Institute for Network Sciences…

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Time-varying Bottleneck Links in LEO Satellite Networks: Identification, Exploits,...

Yangtao Deng (Tsinghua University), Qian Wu (Tsinghua University), Zeqi Lai (Tsinghua University), Chenwei Gu (Tsinghua University), Hewu Li (Tsinghua University), Yuanjie Li (Tsinghua University), Jun Liu (Tsinghua University)

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BrowserFM: A Feature Model-based Approach to Browser Fingerprint Analysis

Maxime Huyghe (Univ. Lille, Inria, CNRS, UMR 9189 CRIStAL), Clément Quinton (Univ. Lille, Inria, CNRS, UMR 9189 CRIStAL), Walter Rudametkin (Univ. Rennes, Inria, CNRS, UMR 6074 IRISA)

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