Skip to main content

ADAM: Automatic Detection of Android Malware

  • Conference paper
  • First Online:
Innovative Security Solutions for Information Technology and Communications (SecITC 2021)

Abstract

The popularity of the Android operating system has been rising ever since its initial release in 2008. This is due to two major reasons. The first is that Android is open-source, due to which a lot of mobile manufacturing companies use some form of modified Android OS for their devices. The second reason is that a wide variety of applications with different designs and utility can be built with ease for Android devices. With this much popularity, gaining unwanted attention of cybercriminals is inevitable. Hence, there has been a huge rise in the number of malware being developed for Android devices. To address this problem, we present ADAM (Automatic Detection of Android Malware), an Android application that uses machine learning (ML) for automatic detection of malware in Android applications. ADAM is trained with CICMalDroid 2020 Android Malware dataset and tested for both CICMalDroid 2020 and CICMalDroid 2017 dataset. The experiment analysis showed that it achieves more than 98.5% accuracy. ADAM considers only static analysis, so becomes easy to deploy in smart phone to alert the user. ADAM is deployed over android mobile phone.

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

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

Notes

  1. 1.

    http://205.174.165.80/CICDataset/MalDroid-2020/Dataset/APKs/.

References

  1. The international data corporation IDC: smartphone OS market share, 2016 Q2 (2016). http://www.idc.com/prodserv/smartphone-os-market-share.jsp

  2. Kaspersky: The number of new malicious files detected every day (2020). https://www.kaspersky.com/about/press-releases/2020_the-number-of-new-malicious-files-detected-every-day-increases-by-52-to-360000-in-2020

  3. Number of android apps on google play. https://www.appbrain.com/stats/number-of-android-apps. Accessed 30 Apr 2020

  4. Afonso, V.M., de Amorim, M.F., Grégio, A.R.A., Junquera, G.B., de Geus, P.L.: Identifying android malware using dynamically obtained features. J. Comput. Virol. Hacking Tech. 11(1), 9–17 (2015)

    Article  Google Scholar 

  5. Alzaylaee, M.K., Yerima, S.Y., Sezer, S.: DL-droid: deep learning based android malware detection using real devices. Comput. Secur. 89, 101663 (2020)

    Google Scholar 

  6. Amankwah, R., Kudjo, P.K., Antwi, S.Y.: Evaluation of software vulnerability detection methods and tools: a review. Int. J. Comput. Appl. 169(8), 22–7 (2017)

    Google Scholar 

  7. Arp, D., Spreitzenbarth, M., Hubner, M., Gascon, H., Rieck, K., Siemens, C.: DREBIN: effective and explainable detection of android malware in your pocket. In: Ndss, vol. 14, pp. 23–26 (2014)

    Google Scholar 

  8. Burguera, I., Zurutuza, U., Nadjm-Tehrani, S.: Crowdroid: behavior-based malware detection system for android. In: Proceedings of the 1st ACM Workshop on Security and Privacy in Smartphones and Mobile Devices, pp. 15–26 (2011)

    Google Scholar 

  9. 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)

    Article  Google Scholar 

  10. Enck, W., et al.: TaintDroid: an information-flow tracking system for realtime privacy monitoring on smartphones. ACM Trans. Comput. Syst. (TOCS) 32(2), 1–29 (2014)

    Article  Google Scholar 

  11. Gao, T., Peng, W., Sisodia, D., Saha, T.K., Li, F., Al Hasan, M.: Android malware detection via graphlet sampling. IEEE Trans. Mob. Comput. 18(12), 2754–2767 (2018)

    Article  Google Scholar 

  12. Glodek, W., Harang, R.: Rapid permissions-based detection and analysis of mobile malware using random decision forests. In: MILCOM 2013–2013 IEEE Military Communications Conference, pp. 980–985. IEEE (2013)

    Google Scholar 

  13. Hui, H., Zhi, Y., Xi, N., Liu, Y.: A weighted voting framework for android app’s vetting based on multiple machine learning models. In: Kutyłowski, M., Zhang, J., Chen, C. (eds.) NSS 2020. LNCS, vol. 12570, pp. 63–78. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-65745-1_4

    Chapter  Google Scholar 

  14. Lashkari, A.H., Kadir, A.F.A., Taheri, L., Ghorbani, A.A.: Toward developing a systematic approach to generate benchmark android malware datasets and classification. In: 2018 International Carnahan Conference on Security Technology (ICCST), pp. 1–7. IEEE (2018)

    Google Scholar 

  15. Li, D., Wang, Z., Xue, Y.: DeepDetector: android malware detection using deep neural network. In: 2018 International Conference on Advances in Computing and Communication Engineering (ICACCE), pp. 184–188. IEEE (2018)

    Google Scholar 

  16. Liu, K., Xu, S., Xu, G., Zhang, M., Sun, D., Liu, H.: A review of android malware detection approaches based on machine learning. IEEE Access 8, 124579–124607 (2020)

    Article  Google Scholar 

  17. McGiff, J., Hatcher, W.G., Nguyen, J., Yu, W., Blasch, E., Lu, C.: Towards multimodal learning for android malware detection. In: 2019 International Conference on Computing, Networking and Communications (ICNC), pp. 432–436. IEEE (2019)

    Google Scholar 

  18. Peiravian, N., Zhu, X.: Machine learning for android malware detection using permission and API calls. In: 2013 IEEE 25th International Conference on Tools with Artificial Intelligence, pp. 300–305. IEEE (2013)

    Google Scholar 

  19. Rahali, A., Lashkari, A.H., Kaur, G., Taheri, L., Gagnon, F., Massicotte, F.: DIDroid: android malware classification and characterization using deep image learning. In: 2020 the 10th International Conference on Communication and Network Security, pp. 70–82 (2020)

    Google Scholar 

  20. Santos, I., Brezo, F., Ugarte-Pedrero, X., Bringas, P.G.: Opcode sequences as representation of executables for data-mining-based unknown malware detection. Inf. Sci. 231, 64–82 (2013)

    Article  MathSciNet  Google Scholar 

  21. Tam, K., Fattori, A., Khan, S., Cavallaro, L.: Copperdroid: automatic reconstruction of android malware behaviors. In: NDSS Symposium 2015, pp. 1–15 (2015)

    Google Scholar 

  22. Tan, D.J., Chua, T.W., Thing, V.L.: Securing android: a survey, taxonomy, and challenges. ACM Comput. Surv. (CSUR) 47(4), 1–45 (2015)

    Article  Google Scholar 

  23. Wu, C., Shi, J., Yang, Y., Li, W.: Enhancing machine learning based malware detection model by reinforcement learning. In: Proceedings of the 8th International Conference on Communication and Network Security, pp. 74–78 (2018)

    Google Scholar 

  24. Xiao, X., Xiao, X., Jiang, Y., Liu, X., Ye, R.: Identifying android malware with system call co-occurrence matrices. Trans. Emerg. Telecommun. Technol. 27(5), 675–684 (2016)

    Article  Google Scholar 

  25. Zhao, L., Li, D., Zheng, G., Shi, W.: Deep neural network based on android mobile malware detection system using opcode sequences. In: 2018 IEEE 18th International Conference on Communication Technology (ICCT), pp. 1141–1147. IEEE (2018)

    Google Scholar 

  26. Zhao, M., Ge, F., Zhang, T., Yuan, Z.: AntiMalDroid: an efficient SVM-based malware detection framework for android. In: Liu, C., Chang, J., Yang, A. (eds.) ICICA 2011. CCIS, vol. 243, pp. 158–166. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-27503-6_22

    Chapter  Google Scholar 

  27. Zhu, H.J., You, Z.H., Zhu, Z.X., Shi, W.L., Chen, X., Cheng, L.: DroidDet: effective and robust detection of android malware using static analysis along with rotation forest model. Neurocomputing 272, 638–646 (2018)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Somanath Tripathy .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2022 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Tripathy, S., Singh, N., Singh, D.N. (2022). ADAM: Automatic Detection of Android Malware. In: Ryan, P.Y., Toma, C. (eds) Innovative Security Solutions for Information Technology and Communications. SecITC 2021. Lecture Notes in Computer Science, vol 13195. Springer, Cham. https://doi.org/10.1007/978-3-031-17510-7_2

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-17510-7_2

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-17509-1

  • Online ISBN: 978-3-031-17510-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics