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

B.P. Sreejith Vignesh and M. Rajesh Babu

International Journal of Networking and Virtual Organisations, 2018, vol. 19, issue 2/3/4, 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.

Keywords: Android; malware application; Pearson correlation coefficient; PCC; principal component analysis; PCA; adaptive neuro-fuzzy inference system; ANFIS. (search for similar items in EconPapers)
Date: 2018
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.inderscience.com/link.php?id=95425 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ids:ijnvor:v:19:y:2018:i:2/3/4:p:257-269

Access Statistics for this article

More articles in International Journal of Networking and Virtual Organisations from Inderscience Enterprises Ltd
Bibliographic data for series maintained by Sarah Parker ().