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

Crisis Management of Android Botnet Detection Using Adaptive Neuro-Fuzzy Inference System 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Annals of Data Science Springer Journals

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

Annals of Data Science , Volume 7 (2) – Jun 17, 2020

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References (31)

Publisher
Springer Journals
Copyright
Copyright © Springer-Verlag GmbH Germany, part of Springer Nature 2020
ISSN
2198-5804
eISSN
2198-5812
DOI
10.1007/s40745-020-00265-1
Publisher site
See Article on Publisher Site

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.

Journal

Annals of Data ScienceSpringer Journals

Published: Jun 17, 2020

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