Mobile Botnet Attacks Detection Using Supervised Learning Algorithms

ABSTRACT In today's era of increased smart device usage, the prevalence of malicious attacks targeting mobile devices has risen significantly. Malware developers aim to infect as many mobile devices as possible, capitalizing on the vulnerabilities present in different operating systems that often host sensitive programs such as educational and banking applications. To combat this issue, this article introduces a mobile botnet detection system that leverages machine learning algorithms such as random forest, k‐nearest neighbor, logistic regression, and decision trees. Additionally, the system incorporates the use of the golden ratio evolutionary algorithm for dimension reduction and feature selection. The proposed method's performance is evaluated using detection accuracy, Precision, F1 Score, and Recall as evaluation criteria on NSL‐KDD, Drebin, and ISCX datasets. The results are compared with evaluation values obtained without applying the golden ratio feature selection algorithm and once again with the proposed method. The findings demonstrate that the proposed method, particularly when combined with the random forest algorithm, achieves the highest accuracy in the desired dataset. Moreover, the decision tree and k‐nearest neighbor algorithms also exhibit superior detection accuracy within the proposed method's framework. By utilizing the proposed method, the training time is reduced through feature selection, enabling swift mobile botnet identification.