Optimizing Android Malware Detection Via Ensemble Learning
Dublin Core
Title
Optimizing Android Malware Detection Via Ensemble Learning
Subject
Android Malware Detection
Machine Learning Models
Base Learners
Ensemble Learner
Reverse Engineering
Description
Android operating system has become very popular, with the highest market share, amongst all other mobile operating systems due to its open source nature and users friendliness. This has brought about an uncontrolled rise in malicious applications targeting the Android platform. Emerging trends of Android malware are employing highly sophisticated detection and analysis avoidance techniques such that the traditional signature-based detection methods have become less potent in their ability to detect new and unknown malware. Alternative approaches, such as the Machine learning techniques have taken the lead for timely zero-day anomaly detections. The study aimed at developing an optimized Android malware detection model using ensemble learning technique. Random Forest, Support Vector Machine, and k-Nearest Neighbours were used to develop three distinct base models and their predictive results were further combined using Majority Vote combination function to produce an ensemble model. Reverse engineering procedure was employed to extract static features from large repository of malware samples and benign applications. WEKA 3.8.2 data mining suite was used to perform all the learning experiments. The results showed that Random Forest had a true positive rate of 97.9%, a false positive rate of 1.9% and was able to correctly classify instances with 98%, making it a strong base model. The ensemble model had a true positive rate of 98.1%, false positive rate of 1.8% and was able to correctly classify instances with 98.16%. The finding shows that, although the base learners had good detection results, the ensemble learner produced a better optimized detection model compared with the performances of those of the base learners.
Creator
Christianah, Abikoye Oluwakemi
Gyunka, Benjamin Aruwa
Oluwatobi, Akande Noah
Source
International Journal of Interactive Mobile Technologies (iJIM); Vol. 14 No. 09 (2020); pp. 61-78
1865-7923
Publisher
International Association of Online Engineering (IAOE), Vienna, Austria
Date
2020-06-17
Rights
Copyright (c) 2020 Akande Noah oluwatobi, Benjamin Aruwa Gyunka, Abikoye Oluwakemi Christianah
Relation
Format
application/pdf
Language
eng
Type
info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Peer-reviewed Article
Identifier
Citation
Abikoye Christianah Oluwakemi, Benjamin Gyunka Aruwa and Akande Oluwatobi Noah, Optimizing Android Malware Detection Via Ensemble Learning, International Association of Online Engineering (IAOE), Vienna, Austria, 2020, accessed November 23, 2024, https://igi.indrastra.com/items/show/1571