Skip to content
Research Article Open access CC BY 4.0

Efficiency of Malware Detection in Android System: A Survey

Maria A. Omer, Subhi R. M. Zeebaree, Mohammed A. M. Sadeeq, Baraa Wasfi Salim, Sanaa x Mohsin, Zryan Najat Rashid, Lailan M. Haji

Asian Journal of Research in Computer Science · pp. 59–69 · Published 12 Apr 2021

10.9734/ajrcos/2021/v7i430189

Abstract

Smart phones are becoming essential in our lives, and Android is one of the most popular operating systems. Android OS is wide-ranging in the mobile industry today because of its open-source architecture. It is a wide variety of applications and basic features. App users tend to trust Android OS to secure data, but it has been shown that Android is more vulnerable and unstable. Identification of Android OS malware has become an emerging research subject of concern. This paper aims to analyze the various characteristics involved in malware detection. It also addresses malware detection methods. The current detection mechanism utilizes algorithms such as Bayesian algorithm, Ada grad algorithm, Naïve Bayes algorithm, Hybrid algorithm, and other algorithms for machine learning to train the sets and find the malware.

Cited by 50

Identification and Defense Technology of Information Security Malicious Software Assisted by Artificial Intelligence

Hefang Gong · International Conference on Computational Intelligence and Communication Networks · 2025

DW-FL-CBBA: A Federated Learning Framework with Hybrid Detection Methods for Android Malware Recognition

Shan Huang, Ruihong Liu, Xing-Xing Liu · International Conference on Parallel and Distributed Systems · 2025

CloudIntellMal: An advanced cloud based intelligent malware detection framework to analyze android applications

Preeti Mishra, Tanmay Jain, Palak Aggarwal · Computers & electrical engineering · 2024

An Efficient Security Testing for Android Application Based on Behavior and Activities Using Improved PCA and DNN-KNN Classifier

Pawan Kumar, Sukhdip Singh · Wireless personal communications · 2024

Aplikasi Exmath: Bagaimana Respon Peserta Didik?

Marisa Devi Isnaini Arifah, Dewi Rahmawati, Seftika Anggraini · MANDALIKA Mathematics and Educations Journal · 2023

Android-manifest extraction and labeling method for malware compilation and dataset creation

Djarot Hindarto, Arko Djajadi · International Journal of Electrical and Computer Engineering (IJECE) · 2023

Web-based Efficiency of Distributed Systems and IoT on Functionality of Smart City Applications

Revella E. A. Armya, L. M. Abdulrahman, N. M. Abdulkareem · Journal of Smart Internet of Things · 2023

Hybrid Multimodal Machine Learning Driven Android Malware Recognition and Classification Model

Arthi. A, K. Aggarwal, R. Karthikeyan · 2023 7th International Conference on Electronics, Communication and Aerospace Technology (ICECA) · 2023

Leveraging Oversampling Techniques in Machine Learning Models for Multi-class Malware Detection in Smart Home Applications

Abdullahi Chowdhury, Mohammad Manzurul Islam, Shahriar Kaisar · International Conference on Trust, Security and Privacy in Computing and Communications · 2023

A Survey of Malware Analysis Using Community Detection Algorithms

Abdelouahab Amira, A. Derhab, E. Karbab · ACM Computing Surveys · 2023

Article metrics

Real usage data collected on this platform.

0

Page views

0

PDF downloads

0

Outbound clicks

50

Citations

Views by country

Approximate, from request IP at view time — not citizenship or institution. Countries with fewer than 5 views are grouped as "Other".

No views recorded yet.

Traffic sources

Referring site, by host.

No traffic recorded yet.

Views and downloads exclude known bots/crawlers. Citations combines this platform's own DOI-resolved index with each external source's own reported total — see Cited by above for individually listed citing works. Last refreshed 0 seconds ago.