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 8, 2024, https://igi.indrastra.com/items/show/1571

Social Bookmarking