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Data-Driven Android Malware Intelligence: A Survey

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Machine Learning for Cyber Security (ML4CS 2019)

Part of the book series: Lecture Notes in Computer Science ((LNSC,volume 11806))

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Abstract

Android has dominated the smartphone market and become the most popular mobile operating system. This rapidly increasing market share of Android has contributed to the boom of Android malware in numbers and in varieties. There exist many techniques which are proposed to accurately detect malware, e.g., software engineering-based techniques and machine learning (ML)-based techniques. In this paper, our main contributions are threefold: We reviewed the existing analysis techniques for Android malware detection; We focused on the code analysis based detection techniques under the ML frameworks; We gave the future research challenges and directions about Android malware analysis.

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Notes

  1. 1.

    https://www.idc.com/promo/smartphone-market-share/os.

  2. 2.

    https://www.gdatasoftware.com/.

  3. 3.

    https://play.google.com/store.

  4. 4.

    https://www.wandoujia.com/.

  5. 5.

    http://www.anzhi.com/.

  6. 6.

    https://www.appbrain.com/stats/number-of-android-apps.

  7. 7.

    https://www.gdatasoftware.com/.

  8. 8.

    https://sable.github.io/soot/.

  9. 9.

    https://www.virustotal.com/.

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Authors and Affiliations

  1. School of Information Technology, Deakin University, Melbourne, Australia

    Junyang Qiu, Wei Luo & Lei Pan

  2. Data61, CSIRO, Sydney, Australia

    Surya Nepal

  3. School of Physics and Electronic Information, Yunnan Normal University, Kunming, China

    Yonghang Tai

  4. School of Software and Electrical Engineering, Swinburne University of Technology, Melbourne, Australia

    Jun Zhang & Yang Xiang

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  1. Junyang Qiu
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  1. Xidian University, Xi’an, China

    Xiaofeng Chen

  2. Fujian Normal University, Fuzhou, China

    Xinyi Huang

  3. Swinburne University of Technology, Hawthorn, VIC, Australia

    Jun Zhang

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Qiu, J. et al. (2019). Data-Driven Android Malware Intelligence: A Survey. In: Chen, X., Huang, X., Zhang, J. (eds) Machine Learning for Cyber Security. ML4CS 2019. Lecture Notes in Computer Science(), vol 11806. Springer, Cham. https://doi.org/10.1007/978-3-030-30619-9_14

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