Enhancing mobile security through threat detection and classification of malware using machine learning algorithm

Abstract

Malware, short for malicious software, is designed to disrupt the normal operation of a computer or mobile device, acquire confidential information, and fraudulently gain access to secure computer networks. With mobile devices increasingly targeted by sophisticated malware, traditional security methods often fall short. This research involves preprocessing a comprehensive dataset, employing SMOTE to balance class distribution, and implementing several machine learning models, including SVM, Random Forest, and XGBoost. The models were evaluated for accuracy, precision, recall, and other metrics. The results revealed that SVM, Logistic Regression, AdaBoost, LightGBM, and XGBoost each achieved an accuracy of 0.95, demonstrating strong capability in distinguishing between benign and malicious applications. Random Forest followed closely with an accuracy of 0.94, while K-Nearest Neighbors (KNN) had the lowest accuracy at 0.91. Ethical considerations such as user privacy, bias mitigation, and transparency were emphasized, alongside a sustainability plan to reduce environmental impact. This study demonstrates the effectiveness of machine learning in mobile security, providing a foundation for further research in optimizing and expanding these methods.