skip to main content
10.1145/3371676.3371685acmotherconferencesArticle/Chapter ViewAbstractPublication PagesiccnsConference Proceedingsconference-collections
research-article

Android Malware Detection Combined with Static and Dynamic Analysis

Authors Info & Claims
Published:13 January 2020Publication History

ABSTRACT

Android System has attracted not only constantly increasing number of smart device users, but also the serious attacks from explosive malicious apps. Consequently, the need to effectively detect Android malware is becoming more and more urgent. In the paper, combing the advantages of static analysis and dynamic analysis, we propose an Android malware detection method based on machine classification. Our experimental results show that the accuracy of the approach meets the requirements of Android malware detection. Subsequently, we apply this approach to perform an interesting detection on the popular apps of different user crowds, and provide some corresponding security advices.

References

  1. https://www.kantarworldpanel.com/global/smartphone-os-market-share/Google ScholarGoogle Scholar
  2. https://www.freebuf.com/articles/paper/179295.htmlGoogle ScholarGoogle Scholar
  3. Priyadarshani M. K. and Sunita V. D. 2015. Two Phase Static Analysis Technique for Android Malware Detection. In Proceedings of the Third International Symposium on Women in Computing and Informatics (WCI '15), Indu Nair (Ed.). ACM, New York, NY, USA, 650--655. DOI=https://doi.org/10.1145/2791405.2791558Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. Gerardo C., Eric M., and Corrado A. 2015. Detecting Android malware using sequences of system calls. In Proceedings of the 3rd International Workshop on Software Development Lifecycle for Mobile (DeMobile 2015). ACM, New York, NY, USA, 13--20. DOI=http://dx.doi.org/10.1145/2804345.2804349Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. https://code.google.com/archive/p/androguard/Google ScholarGoogle Scholar
  6. Suzanna S., Yang, and Alfred A. 2015. Android Malware Static Analysis Techniques. In Proceedings of the 10th Annual Cyber and Information Security Research Conference (CISR '15). ACM, New York, NY, USA, Article 5, 8 pages. DOI=https://doi.org/10.1145/2746266.2746271Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. Huda A. A., Tae O., and Bill S. 2016. Android Malware Detection Using Category-Based Machine Learning Classifiers. In Proceedings of the 17th Annual Conference on Information Technology Education (SIGITE '16). ACM, New York, NY, USA, 54--59. DOI=https://doi.org/10.1145/2978192.2978218Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. https://code.google.com/archive/p/droidbox/Google ScholarGoogle Scholar
  9. Michael S., Felix F., Florian E., and Thomas S. 2013. Mobile-sandbox: having a deeper look into android applications. In Proceedings of the 28th Annual ACM Symposium on Applied Computing (SAC '13). ACM, New York, NY, USA, 1808--1815. DOI=https://doi.org/10.1145/2480362.2480701Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. Lucky O., Enrico M., and Panagiotis A. 2019. MaMaDroid: Detecting Android Malware by Building Markov Chains of Behavioral Models (Extended Version). ACM Trans. Priv. Secur. 22, 2, Article 14 (April 2019), 34 pages. DOI=https://doi.org/10.1145/3313391Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. Mohsen K., Mohammad D., and Ali D. 2018. Application of Machine Learning Algorithms for Android Malware Detection. In Proceedings of the 2018 International Conference on Computational Intelligence and Intelligent Systems (CIIS 2018). ACM, New York, NY, USA, 32--36. DOI=https://doi.org/10.1145/3293475.3293489Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. Wu W. C. and Hung S. H.. 2014. DroidDolphin: a dynamic Android malware detection framework using big data and machine learning. In Proceedings of the 2014 Conference on Research in Adaptive and Convergent Systems (RACS '14). ACM, New York, NY, USA, 247--252. DOI=http://dx.doi.org/10.1145/2663761.2664223Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. Peter Z., Dmitry Z., and Andrew D.. 2017. Detecting Android application malicious behaviors based on the analysis of control flows and data flows. In Proceedings of the 10th International Conference on Security of Information and Networks (SIN '17). ACM, New York, NY, USA, 280--283. DOI= https://doi.org/10.1145/3136825.3140583Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. Sen C., Minhui X., and Tang Z. S. 2016. StormDroid: A Streaminglized Machine Learning-Based System for Detecting Android Malware. In Proceedings of the 11th ACM on Asia Conference on Computer and Communications Security (ASIA CCS '16). ACM, New York, NY, USA, 377--388. DOI=https://doi.org/10.1145/2897845.2897860Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. https://labs.mwrinfosecurity.com/tools/drozer/Google ScholarGoogle Scholar
  16. https://www.virustotal.com/Google ScholarGoogle Scholar
  17. https://bbs.kafan.cn/forum-31-1.htmlGoogle ScholarGoogle Scholar
  18. Dam K. and Tayssir T. 2017. Learning Android Malware. In Proceedings of the 12th International Conference on Availability, Reliability and Security (ARES '17). ACM, New York, NY, USA, Article 59, 9 pages. DOI=https://doi.org/10.1145/3098954.3105826Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. Luke D., Vivek N., and Arun L. 2014. DroidLegacy: Automated Familial Classification of Android Malware. In Proceedings of ACM SIGPLAN on Program Protection and Reverse Engineering Workshop 2014 (PPREW'14). ACM, New York, NY, USA, Article 3, 12 pages. DOI=http://dx.doi.org/10.1145/2556464.2556467Google ScholarGoogle ScholarDigital LibraryDigital Library

Index Terms

  1. Android Malware Detection Combined with Static and Dynamic Analysis

    Recommendations

    Comments

    Login options

    Check if you have access through your login credentials or your institution to get full access on this article.

    Sign in
    • Published in

      cover image ACM Other conferences
      ICCNS '19: Proceedings of the 2019 9th International Conference on Communication and Network Security
      November 2019
      172 pages
      ISBN:9781450376624
      DOI:10.1145/3371676

      Copyright © 2019 ACM

      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 ACM 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]

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 13 January 2020

      Permissions

      Request permissions about this article.

      Request Permissions

      Check for updates

      Qualifiers

      • research-article
      • Research
      • Refereed limited

    PDF Format

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader