Dynamic Malware Analysis Using Machine Learning-Based Detection Algorithms

Abstract

With the increasing popularity of cell phone use, the risk of malware infections on such devices has increased, resulting in financial losses for both individuals and organizations. Current research focuses on the application of machine learning for the detection and classification of these malware programs. Accordingly, the present work uses the frequency of system calls to detect and classify malware using the XGBoost, LightGBM and random forest algorithms. The highest results were obtained with the LightGBM algorithm, achieving 94,1 % precision and 93,9 % accuracy, recall, and F1-score, demonstrating the effectiveness of both machine learning and dynamic malware analysis in mitigating security threats on mobile devices.