Enhanced Malware Detection for Mobile Operating Systems Using Machine Learning and Dynamic Analysis

Mobile smartphone operating systems have garnered widespread popularity due to their open-source nature and high performance.However, the convenience of these systems has also led to a rise in malware distribution.Traditional signature-based detection methods often fail to identify unknown threats, prompting the need for more effective solutions.In this study, we propose an advanced machine learning-based model for detecting malware on smartphones.Our model leverages dynamic and static analysis techniques to select and infer features, followed by a novel feature extraction method using sampling and Principal Component Analysis (PCA) to reduce dimensionality without adversely impacting the accuracy.Experimental results demonstrate the effectiveness of our approach in significantly enhancing malware detection accuracy and efficiency on smartphone operating systems.By analyzing the dynamic behavior of applications and incorporating innovative detection methods, our research contributes to a more robust and proactive approach to smartphone security.Through rigorous evaluation using real-world and synthetic datasets, we validate the efficacy of our model in accurately identifying malware instances and guiding users towards safe application downloads.Overall, our study provides a promising avenue for mitigating the escalating threat of malware on mobile devices.