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/v7i430189Abstract
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
R. Abid, M. Rizwan, P. Veselý · Wireless Communications and Mobile Computing · 2022
Ling Sun, Da-Li Gao · Mathematical Problems in Engineering · 2022
G. Padmavathi, D. Shanmugapriya, A. Roshni · International Conference on Computing for Sustainable Global Development · 2022
Shizhen Jin, Zhaofeng Guo, Dong-Li Liu · Computational Intelligence and Neuroscience · 2022
Wen-Tao Wei, Jie Wang, Zheng Yan · Information Fusion · 2022
Gulistan Ahmead Ismael, A. Salih, Adel al-zebari · Asian Journal of Research in Computer Science · 2021
Arshad A. Hussein, Adel al-zebari, Naaman M Omar · Asian Journal of Research in Computer Science · 2021
Suhaib Jasim Hamdi, Naaman M Omar, Adel al-zebari · Asian Journal of Research in Computer Science · 2021
Waleed A. Mohammad, H. M. Yasin, A. Salih · Asian Journal of Research in Computer Science · 2021
Suhaib Jasim Hamdi, I. Ibrahim, Naaman M Omar · Asian Journal of Research in Computer Science · 2021
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.