A Multi-model Fusion Strategy for Android Malware Detection Based on Machine Learning Algorithms

Abstract

In the digital age, the widespread use of Android devices has led to a surge in security threats, especially malware. Android, as the most popular mobile operating system, is a primary target for malicious actors. Conventional antivirus solutions often fall short in identifying new, modified, or zero-day attacks. To address this, researchers have explored various approaches for Android malware detection, including static and dynamic analysis, as well as machine learning (ML) techniques. However, traditional single-model ML approaches have limitations in generalizing across diverse malware behaviors. To overcome this, a multi-model fusion approach is proposed in this paper. The approach integrates multiple machine learning models, including logistic regression, decision tree, and K-nearest neighbors, to improve detection accuracy. Experimental results demonstrate that the fusion method outperforms individual models, offering a more balanced and robust approach to Android malware detection. This methodology showcases the potential of ensemble techniques in enhancing prediction accuracy, providing valuable insights for future research in cybersecurity.