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

Authors

  • Shuguang Xiong

    Microsoft Inc., Way,Redmond,Washington 98052-6399,United States Of America

  • Huitao Zhang

    Northen Arizona University, San Francisco St, Flagstaff, AZ 86011, United States Of America

DOI:

https://doi.org/10.30564/jcsr.v6i2.6632
Received: 15 May 2024 | Revised: 19 May 2024 | Accepted: 21 May 2024 | Published Online: 31 May 2024

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.

Keywords:

Component; Multi-model fusion; Malware detection; Machine learning

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How to Cite

Xiong, S., & Zhang, H. (2024). A Multi-model Fusion Strategy for Android Malware Detection Based on Machine Learning Algorithms. Journal of Computer Science Research, 6(2), 1–11. https://doi.org/10.30564/jcsr.v6i2.6632

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