GENDroid - a graph-based ensemble classifier for detecting Android malware Online publication date: Mon, 05-Sep-2022
by Shikha Badhani; Sunil Kumar Muttoo
International Journal of Information and Computer Security (IJICS), Vol. 18, No. 3/4, 2022
Abstract: Recent years have witnessed a noticeable growth in the development of stealthy Android-based malware which has led to a pressing need for accurate malware detection systems. In this paper, we propose a graph-based ensemble classifier - GENDroid that performs ensemble learning using different graph-based classification techniques. The proposed classifier combines the predictions of three graph-based base classifiers using majority voting. The main advantage of our proposed classifier is that by combining diverse graph-based classifiers, a more accurate classifier can be learned. We experimentally demonstrate a substantial improvement of our proposed method over the individual graph-based classifiers on three datasets of benign and malicious Android apps. The results are backed up by using statistical tests. The robustness of GENDroid against one of the most widely used anti-forensics techniques - code obfuscation, is also verified empirically. GENDroid is also found to be resilient to the evolution of APIs and achieved very high accuracy.
Online publication date: Mon, 05-Sep-2022
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Information and Computer Security (IJICS):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email firstname.lastname@example.org