Evaluation of Advanced Ensemble Learning Techniques for Android Malware Detection

Android is the most well-known portable working framework having billions of dynamic clients worldwide that pulled in promoters, programmers, and cybercriminals to create malware for different purposes. As of late, wide-running inquiries have been led on malware examination and identification for An...

Full description

Saved in:
Bibliographic Details
Published in:Vietnam journal of computer science 2020-05, Vol.7 (2), p.145-159
Main Authors: Rana, Md. Shohel, Sung, Andrew H.
Format: Article
Language:English
Subjects:
bagging
blending
boosting
drebin
ensemble learning
malware
sbfs
stacking
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Android is the most well-known portable working framework having billions of dynamic clients worldwide that pulled in promoters, programmers, and cybercriminals to create malware for different purposes. As of late, wide-running inquiries have been led on malware examination and identification for Android gadgets while Android has likewise actualized different security controls to manage the malware issues, including a User ID (UID) for every application, framework authorizations. In this paper, we advance and assess various kinds of machine learning (ML) by applying ensemble-based learning systems for identifying Android malware related to a substring-based feature selection (SBFS) strategy for the classifiers. In the investigation, we have broadened our previous work where it has been seen that the ensemble-based learning techniques acquire preferred outcome over the recently revealed outcome by directing the DREBIN dataset, and in this manner they give a solid premise to building compelling instruments for Android malware detection.
ISSN:2196-8888
2196-8896
DOI:10.1142/S2196888820500086