Article: Classifying the malware application in the Android-based smart phones using ensemble-ANFIS algorithm Journal: International Journal of Networking and Virtual Organisations (IJNVO) 2018 Vol.19 No.2/3/4 pp.257 - 269 Abstract: Nowadays, the Android-based smartphones are fastest gaining in the market to date. Due to its open architecture and ease of application programming interfaces (APIs), it becomes fertile dregs for hackers to deploy malware application. This result in the burglary of personal information those are stored in smartphones, without the user knowledge unauthorised sends unintentional short message, and if the infected smart phones operate remotely it leads ways to some other malware attacks. However, many defence mechanisms were introduced against Android malware; it results in inaccuracy of classification. The contribution of this paper to detect and classify the malwares in the manifest file based on ensemble adaptive neuro-fuzzy inference system (ANFIS) technique. This proposed system is divided into three main steps to detect the malware applications they are: 1) features are extraction using the method called principal component analysis (PCA) method; 2) feature selection, using Pearson correlation coefficient (PCC) method; 3) malware applications are classified, using ensemble of ANFIS technique. The proposed system produced the best detecting malware applications classification and accuracy will be highly efficient than the other classification techniques. Inderscience Publishers - linking academia, business and industry through research

Title: Classifying the malware application in the Android-based smart phones using ensemble-ANFIS algorithm

Authors: B.P. Sreejith Vignesh; M. Rajesh Babu

Addresses: Department of Computer Science, Bharathiar University, Coimbatore, Tamil Nadu 641046, India ' Department of CSE, Karpagam College of Engineering, Coimbatore, India

Abstract: Nowadays, the Android-based smartphones are fastest gaining in the market to date. Due to its open architecture and ease of application programming interfaces (APIs), it becomes fertile dregs for hackers to deploy malware application. This result in the burglary of personal information those are stored in smartphones, without the user knowledge unauthorised sends unintentional short message, and if the infected smart phones operate remotely it leads ways to some other malware attacks. However, many defence mechanisms were introduced against Android malware; it results in inaccuracy of classification. The contribution of this paper to detect and classify the malwares in the manifest file based on ensemble adaptive neuro-fuzzy inference system (ANFIS) technique. This proposed system is divided into three main steps to detect the malware applications they are: 1) features are extraction using the method called principal component analysis (PCA) method; 2) feature selection, using Pearson correlation coefficient (PCC) method; 3) malware applications are classified, using ensemble of ANFIS technique. The proposed system produced the best detecting malware applications classification and accuracy will be highly efficient than the other classification techniques.

Keywords: Android; malware application; Pearson correlation coefficient; PCC; principal component analysis; PCA; adaptive neuro-fuzzy inference system; ANFIS.

DOI: 10.1504/IJNVO.2018.095425

International Journal of Networking and Virtual Organisations, 2018 Vol.19 No.2/3/4, pp.257 - 269

Received: 10 Oct 2016
Accepted: 08 Feb 2017
Published online: 04 Oct 2018 *

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Title: Classifying the malware application in the Android-based smart phones using ensemble-ANFIS algorithm

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