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DroidEagle: seamless detection of visually similar Android apps

Published: 22 June 2015 Publication History
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  • Abstract

    Repackaged malware and phishing malware consist 86% [35] of all Android malware, and they significantly affect the Android ecosystem. Previous work use disassembled Dalvik bytecode and hashing approaches to detect repackaged malware, but these approaches are vulnerable to obfuscation attacks and they demand large computational resources on mobile devices. In this work, we propose a novel methodology which uses the layout resources within an app to detect apps which are "visually similar", a common characteristic in repackaged apps and phishing malware. To detect visually similar apps, we design and implement DroidEagle which consists of two sub-systems: RepoEagle and HostEagle. RepoEagle is to perform large scale detection on apps repositories (e.g., apps markets), and HostEagle is a lightweight mobile app which can help users to quickly detect visually similar Android app upon download. We demonstrate the high accuracy and efficiency of DroidEagle: Within 3 hours RepoEagle can detect 1298 visually similar apps from 99 626 apps in a repository. In less than one second, HostEagle can help an Android user to determine whether a downloaded mobile app is a repackaged apps or a phishing malware. This is the first work which provides both speed and scalability in discovering repackaged apps and phishing malware in Android system.

    References

    [1]
    Androguard. https://code.google.com/p/androguard/.
    [2]
    Android signing mechanism. http://developer.android.com/tools/publishing/app-signing.html.
    [3]
    Apktool. https://code.google.com/p/android-apktool/.
    [4]
    Axml. https://code.google.com/p/axml/.
    [5]
    Google play bouncer. http://blog.trendmicro.com/trendlabs-security-intelligence/a-look-at-google-bouncer/.
    [6]
    Smali/baksmali. https://code.google.com/p/smali/.
    [7]
    ssdeep. http://ssdeep.sourceforge.net/.
    [8]
    Virtuous ten studio. http://virtuous-ten-studio.com/.
    [9]
    AppBrain. Number of android applications. http://www.appbrain.com/stats/number-of-android-apps.
    [10]
    J. Crussell, C. Gibler, and H. Chen. Attack of the clones: Detecting cloned applications on android markets. In Computer Security--ESORICS 2012. Springer, 2012.
    [11]
    J. Crussell, C. Gibler, and H. Chen. Andarwin: Scalable detection of semantically similar android applications. In Computer Security--ESORICS 2013. Springer, 2013.
    [12]
    F-Secure. Threat report h2 2013. http://www.f-secure.com/static/doc/labs_global/Research/Threat_Report_H2_2013.pdf.
    [13]
    A. Y. Fu, L. Wenyin, and X. Deng. Detecting phishing web pages with visual similarity assessment based on earth mover's distance (emd). Dependable and Secure Computing, IEEE Transactions on, 2006.
    [14]
    Gartner. Gartner says annual smartphone sales surpassed sales of feature phones for the first time in 2013. http://www.gartner.com/newsroom/id/2665715.
    [15]
    C. Gibler, R. Stevens, J. Crussell, H. Chen, H. Zang, and H. Choi. Adrob: Examining the landscape and impact of android application plagiarism. In Proceeding of the 11th annual international conference on Mobile systems, applications, and services. ACM, 2013.
    [16]
    S. Hanna, L. Huang, E. Wu, S. Li, C. Chen, and D. Song. Juxtapp: A scalable system for detecting code reuse among android applications. In Detection of Intrusions and Malware, and Vulnerability Assessment, pages 62--81. Springer, 2013.
    [17]
    G. Inc. Google play. https://play.google.com.
    [18]
    Kaspersky. Kaspersky security bulletin 2013. overall statistics for 2013. http://media.kaspersky.com/pdf/KSB_2013_EN.pdf.
    [19]
    J. Kornblum. Identifying almost identical files using context triggered piecewise hashing. Digital investigation, 3:91--97, 2006.
    [20]
    W. Liu, X. Deng, G. Huang, and A. Y. Fu. An antiphishing strategy based on visual similarity assessment. Internet Computing, IEEE, 2006.
    [21]
    Y. Pan and X. Ding. Anomaly based web phishing page detection. In Computer Security Applications Conference, 2006. ACSAC'06. 22nd Annual. IEEE, 2006.
    [22]
    V. Rastogi, Y. Chen, and X. Jiang. Droidchameleon: evaluating android anti-malware against transformation attacks. In Proceedings of the 8th ACM SIGSAC symposium on Information, computer and communications security. ACM, 2013.
    [23]
    T. Strazzere. Dex education: Practicing safe dex. In Blackhat USA 2012, 2012.
    [24]
    Umeng. 2013 umeng insight report. http://www.slideshare.net/umengnews/2013-umeng-insight-report.
    [25]
    Yourstory. Google play is not the place to be in china, "app in china" connects you to top 20 chinese android app stores. http://yourstory.com/2014/02/google-play-place-china-app-china-connects-top-20-chinese-android-app-stores/.
    [26]
    C. Zauner. Implementation and benchmarking of perceptual image hash functions. na, 2010.
    [27]
    F. Zhang, H. Huang, S. Zhu, D. Wu, and P. Liu. View-droid: Towards obfuscation-resilient mobile application repackaging detection. In Proceedings of the 7th ACM Conference on Security and Privacy in Wireless and Mobile Networks (WiSec 2014). ACM, 2014.
    [28]
    K. Zhang and D. Shasha. Simple fast algorithms for the editing distance between trees and related problems. SIAM journal on computing, 1989.
    [29]
    M. Zheng, P. P. Lee, and J. C. Lui. Adam: An automatic and extensible platform to stress test android anti-virus systems. In Detection of Intrusions and Malware, and Vulnerability Assessment. Springer, 2013.
    [30]
    M. Zheng, M. Sun, and J. Lui. Droid analytics: A signature based analytic system to collect, extract, analyze and associate android malware. In Trust, Security and Privacy in Computing and Communications (TrustCom), 2013 12th IEEE International Conference on, pages 163--171. IEEE, 2013.
    [31]
    W. Zhou, Z. Wang, Y. Zhou, and X. Jiang. Divilar: diversifying intermediate language for anti-repackaging on android platform. In Proceedings of the 4th ACM conference on Data and application security and privacy. ACM, 2014.
    [32]
    W. Zhou, X. Zhang, and X. Jiang. Appink: watermarking android apps for repackaging deterrence. In Proceedings of the 8th ACM SIGSAC symposium on Information, computer and communications security. ACM, 2013.
    [33]
    W. Zhou, Y. Zhou, M. Grace, X. Jiang, and S. Zou. Fast, scalable detection of piggybacked mobile applications. In Proceedings of the third ACM conference on Data and application security and privacy, pages 185--196. ACM, 2013.
    [34]
    W. Zhou, Y. Zhou, X. Jiang, and P. Ning. Detecting repackaged smartphone applications in third-party android marketplaces. In Proceedings of the second ACM conference on Data and Application Security and Privacy. ACM, 2012.
    [35]
    Y. Zhou and X. Jiang. Dissecting android malware: Characterization and evolution. In Security and Privacy (SP), 2012 IEEE Symposium on. IEEE.

    Cited By

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    • (2023)Mixed Signals: Analyzing Software Attribution Challenges in the Android EcosystemIEEE Transactions on Software Engineering10.1109/TSE.2023.323658249:4(2964-2979)Online publication date: 1-Apr-2023
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    cover image ACM Conferences
    WiSec '15: Proceedings of the 8th ACM Conference on Security & Privacy in Wireless and Mobile Networks
    June 2015
    256 pages
    ISBN:9781450336239
    DOI:10.1145/2766498
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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    Published: 22 June 2015

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    Cited By

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    • (2023)A Survey and Evaluation of Android-Based Malware Evasion Techniques and Detection FrameworksInformation10.3390/info1407037414:7(374)Online publication date: 30-Jun-2023
    • (2023)DeUEDroid: Detecting Underground Economy Apps Based on UTG SimilarityProceedings of the 32nd ACM SIGSOFT International Symposium on Software Testing and Analysis10.1145/3597926.3598051(223-235)Online publication date: 12-Jul-2023
    • (2023)Mixed Signals: Analyzing Software Attribution Challenges in the Android EcosystemIEEE Transactions on Software Engineering10.1109/TSE.2023.323658249:4(2964-2979)Online publication date: 1-Apr-2023
    • (2022)The rise of obfuscated Android malware and impacts on detection methodsPeerJ Computer Science10.7717/peerj-cs.9078(e907)Online publication date: 9-Mar-2022
    • (2022)Research on Third-Party Libraries in Android Apps: A Taxonomy and Systematic Literature ReviewIEEE Transactions on Software Engineering10.1109/TSE.2021.311438148:10(4181-4213)Online publication date: 1-Oct-2022
    • (2022)An Exploratory Study for GUI Posts on Stack Overflow2022 IEEE 22nd International Conference on Software Quality, Reliability and Security (QRS)10.1109/QRS57517.2022.00114(1113-1124)Online publication date: Dec-2022
    • (2022)AndroMalPack: enhancing the ML-based malware classification by detection and removal of repacked apps for Android systemsScientific Reports10.1038/s41598-022-23766-w12:1Online publication date: 14-Nov-2022
    • (2022)GridDroid—An Effective and Efficient Approach for Android Repackaging Detection Based on Runtime Graphical User InterfaceJournal of Computer Science and Technology10.1007/s11390-021-1659-337:1(147-181)Online publication date: 31-Jan-2022
    • (2021)Rebooting Research on Detecting Repackaged Android Apps: Literature Review and BenchmarkIEEE Transactions on Software Engineering10.1109/TSE.2019.290167947:4(676-693)Online publication date: 1-Apr-2021
    • (2021)MDSDroid: A Multi-level Detection System for Android Repackaged Applications2021 IEEE 6th International Conference on Signal and Image Processing (ICSIP)10.1109/ICSIP52628.2021.9688672(1128-1133)Online publication date: 22-Oct-2021
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