Omer, Maria A. and Zeebaree, Subhi R. M. and Sadeeq, Mohammed A. M. and Salim, Baraa Wasfi and Mohsin, Sanaa x and Rashid, Zryan Najat and Haji, Lailan M. (2021) Efficiency of Malware Detection in Android System: A Survey. Asian Journal of Research in Computer Science, 7 (4). pp. 59-69. ISSN 2581-8260
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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.
Item Type: | Article |
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Subjects: | Open Asian Library > Computer Science |
Depositing User: | Unnamed user with email support@openasianlibrary.com |
Date Deposited: | 03 Feb 2023 09:46 |
Last Modified: | 21 Oct 2024 04:18 |
URI: | http://publications.eprintglobalarchived.com/id/eprint/134 |