Open AccessCOMPARATIVE ANALYSIS OF MACHINE LEARNING ALGORITHMS FOR THE DETECTION OF ANDROID MALWARE
Author(s) -
Efefiong Udo-Nya,
AUTHOR_ID,
Olawale Surajudeen Adebayo,
AUTHOR_ID
Publication year - 2021
Publication title -
international journal of innovative research in advanced engineering
Language(s) - English
Resource type - Journals
ISSN - 2349-2163
DOI - 10.26562/ijirae.2021.v0809.004
Subject(s) - support vector machine , computer science , malware , android malware , android (operating system) , subspace topology , machine learning , algorithm , artificial intelligence , operating system
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.