Permission-Based Android Malware Application Detection Using Multi-Layer Perceptron

  • O. S. Jannath NishaEmail author
  • S. Mary Saira Bhanu
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 941)


With the increasing number of Android malwares, there arises a need to develop a system that automatically detects malware in Android applications (apps). To discriminate malware applications (malapps) from benign apps, researchers have proposed several detection techniques to detect malicious apps automatically. However, most of these techniques depend on hand-crafted features which are very difficult to analyze. In this paper, the proposed Android Malware Detection approach uses MultiLayer Perceptron (MLP) to discriminate malware apps from benign ones. This approach uses permissions as features based on the static analysis techniques from a disassembled APK file. Apps permission features are automatically learned by the neural network and thereby removing the need for hand-crafted features and are trained to discriminate apps. It is computationally feasible with a large number of dataset samples to perform classification. After the training, the MLP network can be executed, allowing a substantial amount of files to be detected and providing high accurate classification rate. The proposed approach is evaluated using the benchmark dataset. The experimental results show that the proposed approach can achieve high accuracy than that of the existing techniques.


Android operating system Benign apps Malapps Permissions Neural networks MLP 


  1. 1.
    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
  2. 2.
    Aung, Z., Zaw, W.: Permission-based android malware detection. Int. J. Sci. Technol. Res. 2(3), 228–234 (2013)Google Scholar
  3. 3.
    Brahler, S.: Analysis of the android architecture. Karlsruhe institute for technology 7(8) (2010)Google Scholar
  4. 4.
    Desnos, A.: Androguard: Reverse engineering, malware and goodware analysis of android applications... and more (ninja!) (2015)Google Scholar
  5. 5.
    Faruki, P., Bharmal, A., Laxmi, V., Ganmoor, V., Gaur, M.S., Conti, M., Rajarajan, M.: Android security: a survey of issues, malware penetration, and defenses. IEEE Commun. Surv. Tutor. 17(2), 998–1022 (2015)CrossRefGoogle Scholar
  6. 6.
    Ghorbanzadeh, M., Chen, Y., Ma, Z., Clancy, T.C., McGwier, R.: A neural network approach to category validation of android applications. In: 2013 International Conference on Computing, Networking and Communications (ICNC), pp. 740–744. IEEE (2013)Google Scholar
  7. 7.
    Jha, A.K., Lee, W.J.: Analysis of permission-based security in android through policy expert, developer, and end user perspectives. J. UCS 22(4), 459–474 (2016)Google Scholar
  8. 8.
    Matsudo, T., Kodama, E., Wang, J., Takata, T.: A proposal of security advisory system at the time of the installation of applications on android OS. In: 2012 15th International Conference on Network-Based Information Systems (NBiS), pp. 261–267. IEEE (2012)Google Scholar
  9. 9.
    Moonsamy, V., Rong, J., Liu, S.: Mining permission patterns for contrasting clean and malicious android applications. Future Gener. Comput. Syst. 36, 122–132 (2014)CrossRefGoogle Scholar
  10. 10.
    Polikar, R., Upda, L., Upda, S.S., Honavar, V.: Learn++: an incremental learning algorithm for supervised neural networks. IEEE Trans. Syst. Man Cybern. Part C (Appl. Rev.) 31(4), 497–508 (2001)CrossRefGoogle Scholar
  11. 11.
    Rashidi, B., Fung, C.J.: A survey of android security threats and defenses. JoWUA 6(3), 3–35 (2015)Google Scholar
  12. 12.
    Sahs, J., Khan, L.: A machine learning approach to android malware detection. In: 2012 European Intelligence and Security Informatics Conference (EISIC), pp. 141–147. IEEE (2012)Google Scholar
  13. 13.
    Sharma, A., Dash, S.K.: Mining API calls and permissions for android malware detection. In: International Conference on Cryptology and Network Security, pp. 191–205. Springer (2014)Google Scholar
  14. 14.
    Tchakounté, F.: Permission-based malware detection mechanisms on android: analysis and perspectives. J. Comput. Sci. 1(2) (2014)Google Scholar
  15. 15.
    Wang, W., Wang, X., Feng, D., Liu, J., Han, Z., Zhang, X.: Exploring permission-induced risk in android applications for malicious application detection. IEEE Trans. Inf. Forensics Secur. 9(11), 1869–1882 (2014)CrossRefGoogle Scholar
  16. 16.
    Wang, X., Yang, Y., Zeng, Y.: Accurate mobile malware detection and classification in the cloud. SpringerPlus 4(1), 583 (2015)CrossRefGoogle Scholar
  17. 17.
    Wei, L., Luo, W., Weng, J., Zhong, Y., Zhang, X., Yan, Z.: Machine learning-based malicious application detection of android. IEEE Access 5, 25591–25601 (2017)CrossRefGoogle Scholar
  18. 18.
    Wu, D.J., Mao, C.H., Wei, T.E., Lee, H.M., Wu, K.P.: DroidMat: android malware detection through manifest and API calls tracing. In: 2012 Seventh Asia Joint Conference on Information Security (Asia JCIS), pp. 62–69. IEEE (2012)Google Scholar
  19. 19.
    Xiao, X., Wang, Z., Li, Q., Xia, S., Jiang, Y.: Back-propagation neural network on Markov chains from system call sequences: a new approach for detecting android malware with system call sequences. IET Inf. Secur. 11(1), 8–15 (2016)CrossRefGoogle Scholar
  20. 20.
    Zaidi, S.F.A., Shah, M.A., Kamran, M., Javaid, Q., Zhang, S.: A survey on security for smartphone device. Int. J. Adv. Comput. Sci. Appl. 7(4), 206–219 (2016)Google Scholar
  21. 21.
    Zhou, W., Zhou, Y., Jiang, X., Ning, P.: Detecting repackaged smartphone applications in third-party android marketplaces. In: Proceedings of the Second ACM Conference on Data and Application Security and Privacy, pp. 317–326. ACM (2012)Google Scholar
  22. 22.
    Zhou, Y., Wang, Z., Zhou, W., Jiang, X.: Hey, you, get off of my market: detecting malicious apps in official and alternative android markets. In: NDSS, vol. 25, pp. 50–52 (2012)Google Scholar

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© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.National Institute of TechnologyTiruchirappalliIndia

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