Please use this identifier to cite or link to this item:
http://ir.futminna.edu.ng:8080/jspui/handle/123456789/14824| Title: | Comparative Analysis of Machine Learning Algorithms for the Detection of Android Malware |
| Authors: | Efefiong, Udo-Nya Adebayo, Olawale Surajudeen |
| Keywords: | Android Platform Machine Learning, Classification Malware SVM Ensemble Method Mobile Device |
| Issue Date: | Aug-2021 |
| Publisher: | International Journal of Innovative Research in Advance Engineering (IJIRAE) |
| Citation: | Efefiong Udo-Nya, Olawale Surajudeen Adebayo (2021). Comparative Analysis of Machine Learning Algorithms for the Detection of Android Malware. International Journal of Innovative Research in Advance Engineering (IJIRAE): Volume 8(9). Available at https://doi.org/10.26562/ijirae.2021.v0809.004. |
| Abstract: | This paper examines the effectiveness of some machine learning algorithms in the detection of android malicious application. In order to carry out this analysis, drebin dataset of android malicious and good applications were obtained and used for the classification as described in a section of this article. The classification results show that the Cubic SVM, Quadratic SVM and ensemble Subspace KNN performed better with 99.2%, 98.7% and 98.4% accuracy with 0.0079, 0.0129 and 0.1598 error rate respectively. |
| URI: | http://repository.futminna.edu.ng:8080/jspui/handle/123456789/14824 |
| ISSN: | 26562 |
| Appears in Collections: | Cyber Security Science |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| IJIRAE Efe paper.pdf | COMPARATIVE ANALYSIS OF MACHINE LEARNING ALGORITHMS FOR THE DETECTION OF ANDROID MALWARE | 98.19 kB | Adobe PDF | View/Open |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.