Skip to main content
SpringerLink
Log in

Android Malware Detection: A Literature Review

  • Conference paper
  • First Online:
Ubiquitous Security (UbiSec 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1768))

Included in the following conference series:

Abstract

Mobile applications are increasingly being used to support critical domains such as health, logistics, and banking, to name a few. These mobile apps, hence, became a target for malware attackers. Android is an open-source operating system, which runs apps that can be downloaded from official or third-party app stores. Malware exploits these applications to penetrate mobile devices in different ways for different purposes. To address this, different approaches for malware analysis have been proposed for the detection of malware, ranging from pre-installation to post-installation. This paper presents a literature review of recent malware detection approaches and methods. 21 prominent studies, that report three most common approaches, are identified and reviewed. Challenges, limitations, and research directions are identified and discussed. Findings show most studies focus on malware classification and detection, but lack studies that investigate securing apps and detecting vulnerabilities that malware exploits to stealth into mobile apps and devices. They also show that most studies focused on enhancing machine learning models rather than the malware analysis process.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. A threat analysis of sideloading. https://www.apple.com/privacy/docs/Building_a_Trusted_Ecosystem_for_Millions_of_Apps_A_Threat_Analysis_of_Sideloading.pdf. (Accessed 28 August 2022)

  2. Alzubaidi, A.: Recent advances in android mobile malware detection: A systematic literature review. IEEE Access 9 (2021). https://ieeexplore.ieee.org/document/9585476/, https://ieeexplore.ieee.org/stamp/stamp.jsp?tp= &arnumber=9585476

  3. Android vs. ios security comparison (2022). https://www.kaspersky.com/resource-center/threats/android-vs-iphone-mobile-security. (Accessed 28 August 2022)

  4. Arora, A., Peddoju, S.K., Conti, M.: Permpair: Android malware detection using permission pairs. IEEE Trans. Inf. Forensics Sec. 15, 1968–1982 (2019)

    Google Scholar 

  5. Bai, H., Liu, G., Liu, W., Quan, Y., Huang, S.: N-gram, semantic-based neural network for mobile malware network traffic detection. Sec. Commun. Netw. 2021 (2021)

    Google Scholar 

  6. Bhatia,T., Kaushal, R.: Malware detection in android based on dynamic analysis. In: 2017 International Conference on Cyber Security And Protection Of Digital Services (Cyber Security), pp. 1–6. IEEE (2017)

    Google Scholar 

  7. Cai, H., Meng, N., Ryder, B., Yao, D.: Droidcat: Effective android malware detection and categorization via app-level profiling. IEEE Trans. Inf. Forensics Secur. 14(6), 1455–1470 (2018)

    Google Scholar 

  8. Chen, Y.-C., Chen, H.-Y., Takahashi, T., Sun, B., Lin, T.-N.: Impact of code deobfuscation and feature interaction in android malware detection. IEEE Access 9, 123208–123219 (2021)

    Google Scholar 

  9. Feng, P., Ma, J., Sun, C., Xinpeng, X., Ma, Y.: A novel dynamic android malware detection system with ensemble learning. IEEE Access 6, 30996–31011 (2018)

    Google Scholar 

  10. Garg, S., Peddoju, S.K., Sarje, A.K.: Network-based detection of android malicious apps. Int. J. Inf. Sec. 16(4), 385–400 (2017)

    Google Scholar 

  11. Guerra-Manzanares, A., Bahsi, H., Nõmm, S.: Kronodroid: Time-based hybrid-featured dataset for effective android malware detection and characterization. Comput. Sec. 110 (2021)

    Google Scholar 

  12. Hadiprakoso, R.B., Kabetta, H., Buana, I.K.S.: Hybrid-based malware analysis for effective and efficiency android malware detection. In: 2020 International Conference on Informatics, Multimedia, Cyber and Information System (ICIMCIS), pp. 8–12. IEEE (2020)

    Google Scholar 

  13. Ilham, S., Abderrahim, G., Abdelhakim, B.A.: Permission based malware detection in android devices. In: Proceedings of the 3rd International Conference on Smart City Applications, pp. 1–6(2018)

    Google Scholar 

  14. Kambar, M.E.Z.N., Esmaeilzadeh, A., Kim, Y., Taghva, K.: A survey on mobile malware detection methods using machine learning. IEEE (2022). https://ieeexplore.ieee.org/document/9720753/

  15. Karbab, E.B., Debbabi, M., Derhab, A., Mouheb, D.: Data-driven fingerprinting and threat intelligence. In: Springer, Android Malware Detection Using Machine Learning (2021)

    Google Scholar 

  16. Karbab, E.B., Debbabi, M., Derhab, A., Mouheb, D.: Maldozer: Automatic framework for android malware detection using deep learning. Digital Investigation 24, S48–S59 (2018)

    Google Scholar 

  17. Kato, H., Sasaki, T., Sasase, I.: Android malware detection based on composition ratio of permission pairs. IEEE Access 9, 130006–130019 (2021)

    Google Scholar 

  18. Kim, Y.-k., Lee, J.J., Go, M.-H., Kang, H.-Y., Lee, K.: A systematic overview of the machine learning methods for mobile malware detection. In: Security and Communication Networks, vol. 2022 (2022)

    Google Scholar 

  19. Li, W., Cai, J., Wang, Z., Cheng, S.: A robust malware detection approach for android system based on ensemble learning. In Wang, G., Choo, KK.R., Ko, R.K.L., Xu, Y., Crispo, B., (eds.) Ubiquitous Security - First International Conference, UbiSec 2021, Guangzhou, China, December 28–31, 2021, Revised Selected Papers, volume 1557. CCIS, pages 309–321. Springer (2022). https://doi.org/10.1007/978-981-19-0468-4_23

  20. McLaughlin, N., et al.: Deep android malware detection. In: Proceedings of the seventh ACM on Conference On Data And Application Security And Privacy, pp. 301–308 (2017)

    Google Scholar 

  21. Meijin, L., et al.: A systematic overview of android malware detection. Appl. Artif. Intell. 36(1), 2007327 (2022)

    Google Scholar 

  22. Millar, S., McLaughlin, N., del Rincon, J.M., Miller, P.: Multi-view deep learning for zero-day android malware detection. J. Inf. Sec. Appli. 58 (2021)

    Google Scholar 

  23. Mobile malware statistics for q1 2022 | securelist. https://securelist.com/it-threat-evolution-in-q1-2022-mobile-statistics/106589/. (Accessed 28 June 2022)

  24. Mobile operating system market share worldwide | statcounter global stats. https://gs.statcounter.com/os-market-share/mobile/worldwide. (Accessed 28 June 2022)

  25. Arif, J.M., et al.: Android mobile malware detection using fuzzy ahp. J. Inf. Sec. Appli. 61 (2021)

    Google Scholar 

  26. Muttoo, S.K., Badhani, S.: Android malware detection: state of the art. Int. J. Inf. Technol. 9(1), 111–117 (2017). https://doi.org/10.1007/s41870-017-0010-2

    Article  Google Scholar 

  27. Muzaffar, A., Hassen, H.R., Lones, M.A., Zantout, H.: An in-depth review of machine learning based android malware detection. Comput. Sec. 102833 (2022)

    Google Scholar 

  28. Ngamwitroj, S., Limthanmaphon, B.: Adaptive android malware signature detection. In: Proceedings of the 2018 International Conference on Communication Engineering and Technology, pp. 22–25 (2018)

    Google Scholar 

  29. Qiu, J., Zhang, J., Luo,W., Pan, L., Nepal, S., Xiang, Y.: A survey of android malware detection with deep neural models. ACM Comput. Surv. 53, 1–36 (2021–11). ISSN 0360–0300. https://doi.org/10.1145/3417978, https://dl.acm.org/doi/10.1145/3417978, https://sci-hub.se/10.1145/3417978

  30. Rani, S.S., Eric, P.V., Sahithya, P., Priyadharshini, S., Ramyashree, S.: Pro-shield protect: Survey paper for malware detection in android application. IEEE (2022). https://ieeexplore.ieee.org/document/9743038/

  31. Razgallah, A., Khoury, R., Hallé, S., Khanmohammadi, K.: A survey of malware detection in android apps: Recommendations and perspectives for future research. Comput. Sci. Rev. 39 (2021–02). ISSN 15740137. https://doi.org/10.1016/J.COSREV.2020.100358

  32. Sihag, V., Swami, A., Vardhan, M., Singh, P.: Signature based malicious behavior detection in android. In: International Conference on Computing Science, Communication and Security, pp. 251–262. Springer (2020). https://doi.org/10.1007/978-981-15-6648-6_20

  33. Heena Kauser, S.k., Maria Anu, V.: A literature review on android mobile malware detection using machine learning techniques. IEEE (2022). https://ieeexplore.ieee.org/document/9753746/

  34. Somarriba, O., Zurutuza, U.: A collaborative framework for android malware detection using dns & dynamic analysis. In: 2017 IEEE 37th Central America and Panama Convention (CONCAPAN XXXVII), pp 1–6. IEEE (2017)

    Google Scholar 

  35. Tidke, S.K., Karde, P.P., Thakare, V.: Detection and prevention of android malware thru permission analysis. In: 2018 Fourth International Conference on Computing Communication Control and Automation (ICCUBEA), pp. 1–6. IEEE (2018)

    Google Scholar 

  36. Ünver, H.M., Bakour, K.: Android malware detection based on image-based features and machine learning techniques. SN Appli. Sci. 2(7), 1–15 (2020). https://doi.org/10.1007/s42452-020-3132-2

  37. Urooj, B., Shah, M.A., Maple, C., Abbasi, M.K., Riasat, S.: Malware detection: A framework for reverse engineered android applications through machine learning algorithms. IEEE Access (2022). https://ieeexplore.ieee.org/document/9703375/

  38. Wang, H., Zhang, W., He, H.: You are what the permissions told me! android malware detection based on hybrid tactics. J. Inf. Sec. Appli. 66 (2022)

    Google Scholar 

  39. Wang, S., Yan, Q., Chen, Z., Yang, B., Zhao, C., Conti, M.: Detecting android malware leveraging text semantics of network flows. IEEE Trans. Inf. Forensics Secur. 13(5), 1096–1109 (2017)

    Google Scholar 

  40. Wu, Q., Zhu, X., Liu, B.: A survey of android malware static detection technology based on machine learning. Mobile Inf. Syst. 2021 (2021)

    Google Scholar 

  41. Xing, X., et al.: A Malware Detection Approach Using Autoencoder in Deep Learning". In: IEEE Access 10 (2022). https://ieeexplore.ieee.org/document/9723074/, https://ieeexplore.ieee.org/stamp/stamp.jsp?tp= &arnumber=9723074

  42. Yadav, P., Menon, N., Ravi, V., Vishvanathan, S., Pham, T.D.: Efficientnet convolutional neural networks-based android malware detection. Comput. Sec. 115 (2022)

    Google Scholar 

  43. Zhang, H., Luo, S., Zhang, Y., Pan, L.: An efficient android malware detection system based on method-level behavioral semantic analysis. IEEE Access 7, 69246–69256 (2019)

    Article  Google Scholar 

  44. Zhang, N., Xue, J., Ma, Y., Zhang, R., Liang, T., Tan, Y.: Hybrid sequence-based android malware detection using natural language processing. Int. J. Intell. Syst. 36(10), 5770–5784 (2021)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science, Birzeit University, Birzeit, Palestine

    Ahmed Sabbah, Adel Taweel & Samer Zein

Authors
  1. Ahmed Sabbah
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Adel Taweel
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Samer Zein
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ahmed Sabbah .

Editor information

Editors and Affiliations

  1. Guangzhou University, Guangzhou, China

    Guojun Wang

  2. University of Texas at San Antonio, San Antonio, TX, USA

    Kim-Kwang Raymond Choo

  3. Temple University, Philadelphia, PA, USA

    Jie Wu

  4. Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates

    Ernesto Damiani

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Sabbah, A., Taweel, A., Zein, S. (2023). Android Malware Detection: A Literature Review. In: Wang, G., Choo, KK.R., Wu, J., Damiani, E. (eds) Ubiquitous Security. UbiSec 2022. Communications in Computer and Information Science, vol 1768. Springer, Singapore. https://doi.org/10.1007/978-981-99-0272-9_18

Download citation

  • DOI: https://doi.org/10.1007/978-981-99-0272-9_18

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-0271-2

  • Online ISBN: 978-981-99-0272-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation