Harvesting Runtime Values in Android Applications That Feature Anti-Analysis Techniques
Siegfried Rasthofer, Steven Arzt, Marc Miltenberger, Eric Bodden
摘要
It is generally challenging to tell apart malware from benign applications. To make this decision, human analysts are frequently interested in runtime values: targets of reflective method calls, URLs to which data is sent, target telephone numbers of SMS messages, and many more. However, obfuscation and string encryption, used by malware as well as goodware, often not only render human inspections, but also static analyses ineffective. In addition, malware frequently tricks dynamic analyses by detecting the execution environment emulated by the analysis tool and then refraining from malicious behavior. In this work we therefore present HARVESTER, an approach to fully automatically extract runtime values from Android applications. HARVESTER is designed to extract values even from highly obfuscated state-of-the-art malware samples that obfuscate method calls using reflection, hide sensitive values in native code, load code dynamically and apply anti-analysis techniques. The approach combines program slicing with code generation and dynamic execution. Experiments on 16,799 current malware samples show that HARVESTER fully automatically extracts many sensitive values, with perfect precision. The process usually takes less than three minutes and does not require human interaction. In particular, it goes without simulating UI inputs. Two case studies further show that by integrating the extracted values back into the app, HARVESTER can increase the recall of existing static and dynamic analysis tools such as FlowDroid and TaintDroid. Permission to freely reproduce all or part of this paper for noncommercial purposes is granted provided that copies bear this notice and the full citation on the first page. Reproduction for commercial purposes is strictly prohibited without the prior written consent of the Internet Society, the first-named author (for reproduction of an entire paper only), and the author's employer if the paper was prepared within the scope of employment.
问问这篇 Paper
智能体会读完全文。
Lune 把这篇 Paper 索引到了每一个公式,引用它的顶会 Paper 也一样。你提问,回答直接引用原文。
引用它的顶会 Paper24
- ContexloT: Towards Providing Contextual Integrity to Appified IoT PlatformsYunhan Jack Jia, Qi Alfred Chen, Shiqi Wang, Amir Rahmati 等NDSS 2017 · 被引用 325 次
- TriggerScope: Towards Detecting Logic Bombs in Android ApplicationsYanick Fratantonio, Antonio Bianchi, William K. Robertson, Engin Kirda 等S&P 2016 · 被引用 161 次
- Mind Your Weight(s): A Large-scale Study on Insufficient Machine Learning Model Protection in Mobile AppsZhichuang Sun, Ruimin Sun, Long Lu, Alan MisloveUSENIX Security 2021 · 被引用 101 次
- JN-SAF: Precise and Efficient NDK/JNI-aware Inter-language Static Analysis Framework for Security Vetting of Android Applications with Native CodeFengguo Wei, Xingwei Lin, Xinming Ou, Ting Chen 等CCS 2018 · 被引用 93 次
- Malton: Towards On-Device Non-Invasive Mobile Malware Analysis for ARTLei Xue, Yajin Zhou, Ting Chen, Xiapu Luo 等USENIX Security 2017 · 被引用 81 次
相关 Paper
- IntelliDroid: A Targeted Input Generator for the Dynamic Analysis of Android MalwareMichelle Y. Wong, David LieNDSS 2016 · 被引用 253 次
- Tackling runtime-based obfuscation in Android with TIROMichelle Y. Wong, David LieUSENIX Security 2018 · 被引用 59 次
- The impact of tool configuration spaces on the evaluation of configurable taint analysis for AndroidAustin Mordahl, Shiyi WeiISSTA 2021 · 被引用 13 次
- Things You May Not Know About Android (Un)Packers: A Systematic Study based on Whole-System EmulationYue Duan, Mu Zhang, Abhishek Vasisht Bhaskar, Heng Yin 等NDSS 2018 · 被引用 87 次
- Dark Hazard: Learning-based, Large-Scale Discovery of Hidden Sensitive Operations in Android AppsXiaorui Pan, Xueqiang Wang, Yue Duan, XiaoFeng Wang 等NDSS 2017 · 被引用 69 次
