Permission-Based Malware Detection System for Android Using Machine Learning Techniques-Reference-Cited by-同舟云学术

Permission-Based Malware Detection System for Android Using Machine Learning Techniques

Published:2019-01 Issue:01 Volume:29 Page:43-61
ISSN:0218-1940
Container-title:International Journal of Software Engineering and Knowledge Engineering
language:en
Short-container-title:Int. J. Soft. Eng. Knowl. Eng.

Author:

Arslan Recep Sinan¹,Doğru İbrahim Alper¹,Barişçi Necaattin¹

Affiliation:

1. Department of Computer Engineering, Gazi University, Emniyet District Gazi University Rectorate, Ankara 06560, Turkey

Abstract

Mobile applications create their own security and privacy models through permission-based models. Some applications may request extra permissions that they do not need but may use for suspicious activities. The aim of this study is to identify those spare permissions requested and use this information in the security and privacy approach, which uses static and code analysis together and applies them to the existing datasets; then the results are compared and accuracy level is determined. Classification is made with an accuracy rate of 91.95%.

Publisher

World Scientific Pub Co Pte Lt

Subject

Artificial Intelligence,Computer Graphics and Computer-Aided Design,Computer Networks and Communications,Software

Link

https://www.worldscientific.com/doi/pdf/10.1142/S0218194019500037

Reference26 articles.

1. Detecting mobile malware threats to homeland security through static analysis

2. Security aspects of mobile phone virus: a critical survey

3. A Permission verification approach for android mobile applications

4. Mobile application security: malware threats and defenses

Cited by 38 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. A Detection Android Cybercrime Model utilizing Machine Learning Technology;Engineering, Technology & Applied Science Research;2024-08-02

2. Leveraging Machine Learning for Android Malware Analysis: Insights from Static and Dynamic Techniques;Engineering, Technology & Applied Science Research;2024-08-02

3. An effective attention and residual network for malware detection;IET Communications;2024-04-27

4. MFEMDroid: A Novel Malware Detection Framework Using Combined Multitype Features and Ensemble Modeling;IET Information Security;2024-02-17

5. A multi-model ensemble learning framework for imbalanced android malware detection;Expert Systems with Applications;2023-12