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VAHunt: Warding Off New Repackaged Android Malware in App-Virtualization's Clothing

Published: 02 November 2020 Publication History

Abstract

Repackaging popular benign apps with malicious payload used to be the most common way to spread Android malware. Nevertheless, since 2016, we have observed an alarming new trend to Android ecosystem: a growing number of Android malware samples abuse recent app-virtualization innovation as a new distribution channel. App-virtualization enables a user to run multiple copies of the same app on a single device, and tens of millions of users are enjoying this convenience. However, cybercriminals repackage various malicious APK files as plugins into an app-virtualization platform, which is flexible to launch arbitrary plugins without the hassle of installation. This new style of repackaging gains the ability to bypass anti-malware scanners by hiding the grafted malicious payload in plugins, and it also defies the basic premise embodied by existing repackaged app detection solutions.
As app-virtualization-based apps are not necessarily malware, in this paper, we aim to make a verdict on them prior to run time. Our in-depth study results in two key observations: 1) the proxy layer between plugin apps and the Android framework is the core of app-virtualization mechanism, and it reveals the feature of finite state transitions; 2) malware typically loads plugins stealthily and hides malicious behaviors. These insights motivate us to develop a two-layer detection approach, called VAHunt. First, we design a stateful detection model to identify the existence of an app-virtualization engine in APK files. Second, we perform data flow analysis to extract fingerprinting features to differentiate between malicious and benign loading strategies. Since October 2019, we have tested VAHunt in Antiy AVL Mobile Security, a leading mobile security company, to detect more than 139K app-virtualization-based samples. Compared with the ground truth, VAHunt achieves 0.7% false negatives and zero false positive. Our automated detection frees security analysts from the burden of reverse engineering.

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    cover image ACM Conferences
    CCS '20: Proceedings of the 2020 ACM SIGSAC Conference on Computer and Communications Security
    October 2020
    2180 pages
    ISBN:9781450370899
    DOI:10.1145/3372297
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    Published: 02 November 2020

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    Author Tags

    1. android malware
    2. app-virtualization
    3. stateful static analysis
    4. stealthy behavior

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    • (2024)Risky Cohabitation: Understanding and Addressing Over-privilege Risks of Commodity Application Virtualization Platforms in AndroidProceedings of the Fourteenth ACM Conference on Data and Application Security and Privacy10.1145/3626232.3653274(253-264)Online publication date: 19-Jun-2024
    • (2024)DOMR: Toward Deep Open-World Malware RecognitionIEEE Transactions on Information Forensics and Security10.1109/TIFS.2023.333846919(1455-1468)Online publication date: 1-Jan-2024
    • (2024)Refining Automation in Power Dispatching Systems: A Cloud Optimization Investigation2024 International Conference on Inventive Computation Technologies (ICICT)10.1109/ICICT60155.2024.10544445(1899-1904)Online publication date: 24-Apr-2024
    • (2024)Maloid-DS: Labeled Dataset for Android Malware ForensicsIEEE Access10.1109/ACCESS.2024.340021112(73481-73546)Online publication date: 2024
    • (2024)RT-APT: A Real-time APT Anomaly Detection Method for Large-scale Provenance GraphJournal of Network and Computer Applications10.1016/j.jnca.2024.104036(104036)Online publication date: Oct-2024
    • (2023)VEDRANDO: A Novel Way to Reveal Stealthy Attack Steps on Android through Memory ForensicsJournal of Cybersecurity and Privacy10.3390/jcp30300193:3(364-395)Online publication date: 10-Jul-2023
    • (2023)An Approach to Reveal Unknown Malware Hiding Techniques2023 IEEE/ACIS 8th International Conference on Big Data, Cloud Computing, and Data Science (BCD)10.1109/BCD57833.2023.10466287(245-249)Online publication date: 14-Dec-2023
    • (2023)Experimental comparison of features, analyses, and classifiers for Android malware detectionEmpirical Software Engineering10.1007/s10664-023-10375-y28:6Online publication date: 26-Sep-2023
    • (2022)Rotten apples spoil the bunchProceedings of the 44th International Conference on Software Engineering10.1145/3510003.3510161(1919-1931)Online publication date: 21-May-2022
    • (2022)Research on Multiple Detection Method Against Mascara Attack2022 3rd International Conference on Computer Vision, Image and Deep Learning & International Conference on Computer Engineering and Applications (CVIDL & ICCEA)10.1109/CVIDLICCEA56201.2022.9825101(851-855)Online publication date: 20-May-2022
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