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Crisis Management of Android Botnet Detection Using Adaptive Neuro-Fuzzy Inference System

Author

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  • Vojo Lakovic

    (Fakulteta društvenih znanosti dr. Milenka Brkića)

Abstract

Android has become a leader in market share of mobile operating systems. In addition, it has attracted interest of attackers greater than other running systems. As a result, Android malware is developing rapidly. In this study, the foremost focal point is on inspecting and detecting botnets that are particular type of malwares. To analyze android botnet detection, it is appropriate to pick out and analyze elements which are enormously applicable or most influential to the botnet detection. This technique typically named variable determination is corresponds to determine a subset of complete recorded variables which presents favorable predictions capability. In this research work, architecture based totally upon adaptive neuro-fuzzy inference system (ANFIS) is used to model complex systems in function approximation and regression. ANFIS community is employed to operate variable selection for identifying that how the botnet have an effect on the android. After deciding on the two most influential parameters, the ANFIS is utilized to create a machine for android botnet detection on the foundation of the chosen parameters.

Suggested Citation

  • Vojo Lakovic, 2020. "Crisis Management of Android Botnet Detection Using Adaptive Neuro-Fuzzy Inference System," Annals of Data Science, Springer, vol. 7(2), pages 347-355, June.
    • Handle: RePEc:spr:aodasc:v:7:y:2020:i:2:d:10.1007_s40745-020-00265-1
    DOI: 10.1007/s40745-020-00265-1
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    Cited by:

    1. Wasim Akram Mandal, 2023. "Bipolar Pythagorean Fuzzy Sets and Their Application in Multi-attribute Decision Making Problems," Annals of Data Science, Springer, vol. 10(3), pages 555-587, June.

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