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


  • 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


Received: 15 May 2024 | Revised: 19 May 2024 | Accepted: 21 May 2024 | Published Online: 31 May 2024


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.


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


[1] Zhou, L., Luo, Z., Pan, X., 2024. Machine learning-based system reliability analysis with Gaussian Process Regression. arXiv preprint arXiv:2403.11125.

[2] Pan, X., Luo, Z., Zhou, L., 2024. Navigating the landscape of distributed file systems: Architectures, implementations, and considerations. arXiv preprint arXiv:2403.15701.

[3] Qiu, Y., Wang, J., Jin, Z., Chen, H., Zhang, M., Guo, L., 2022. Pose-guided matching based on deep learning for assessing quality of action on rehabilitation training. Biomedical Signal Processing and Control, 72, 103323.

[4] Chen, F., et al., 2024. Comprehensive Survey of Model Compression and Speed up for Vision Transformers. arXiv preprint arXiv:2404.10407.

[5] Zhou, L., Wang, M., Zhou, N., 2024. Distributed Federated learning-based deep learning model for privacy MRI brain tumor detection. arXiv preprint arXiv:2404.10026.

[6] Zhou, L., Zhang, H., Zhou, N., 2024. Double-compressed artificial neural network for efficient model storage in customer churn prediction. Artificial Intelligence Advances. 6(1), 1-12.

[7] Liu, Y., et al., 2021. Measuring distance using ultra-wideband radio technology enhanced by extreme gradient boosting decision tree (XGBoost). Automation in Construction, 126, 103678.

[8] Liu, Y., Bao, Y., 2023. Real-time remote measurement of distance using ultra-wideband (UWB) sensors. Automation in Construction, 150, 104849.

[9] Arshad, S., et al., 2016. Android malware detection and protection: a survey. International Journal of Advanced Computer Science and Applications, 7(2), 463–475.

[10] Rashidi, B., Fung, C.J., 2015. A survey of android security threats and defenses. Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications, 6(3), 3-35.

[11] Arshad, S., Shah, M. A., Khan, A., Ahmed, M., 2016. Android malware detection and protection: a survey. International Journal of Advanced Computer Science and Applications, 7(2), 463–475.

[12] Rashidi, B., Fung, C.J., 2015. A survey of Android security threats and defenses. Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications. 6(3), 3–35.

[13] Li, L., et al., 2017. Static analysis of Android apps: A systematic literature review. Information and Software Technology. 88, 67–95.

[14] Tam, K., Feizollah, A., Anuar, N. B., Salleh, R., Cavallaro, L., 2017. The evolution of Android malware and Android analysis techniques. Computing Surveys. 49(4), 76:1–76:41.

[15] Faruki, P., et al., 2015. Android security: a survey of issues, malware penetration, and defenses. IEEE Communications Surveys and Tutorials. 17(2), 998–1022.

[16] Alqahtani, E.J., Zagrouba, R., Almuhaideb, A., 2019. A survey on Android malware detection techniques using machine learning algorithms. Proceedings of the 6th International Conference on Software Defined Systems. 110–117.

[17] Souri, A., Hosseini, R., 2018. A state-of-the-art survey of malware detection approaches using data mining techniques. Human-centric Computing and Information Sciences. 8(1), 3.

[18] Faruki, P., et al., 2015. Android security: A survey of issues, malware penetration, and defenses. IEEE Communications Surveys and Tutorials. 17(2), 998–1022.

[19] Felt, A. P., Finifter, M., Chin, E., Hanna, S., Wagner, D., 2011. A survey of mobile malware in the wild. In Proceedings of the 1st ACM Workshop on Security and Privacy in Smartphones and Mobile Devices (SPSM) (pp. 3–14).

[20] Android Security and Privacy. (2018). 2018 Year In Review. [cited April 30, 2020]. Available from: https://source.android.com/security/reports/Google_Android_Security_2018_Report_Final.pdf

[21] Zhou, Y., Jiang, X., 2012. Dissecting Android malware: Characterization and evolution. In Proceedings of the IEEE Symposium on Security and Privacy (pp. 95–109).

[22] Protsenko, M., Muller, T., 2014. Android malware detection based on software complexity metrics. In Proceedings of the International Conference on Trust, Privacy & Security in Digital Business (pp. 24–35).

[23] Yang, C., et al., 2014. DroidMiner: Automated mining and characterization of fine-grained malicious behaviors in Android applications. In Proceedings of the European Symposium on Research in Computer Security (ESORICS) (pp. 163–182).

[24] Xu, K., Li, Y., Deng, R.H., 2016. ICCDetector: ICC-based malware detection on Android. IEEE Transactions on Information Forensics and Security. 11(6), 1252–1264.

[25] Yang, M., Wen, Q., 2017. Detecting Android malware by applying classification techniques on images patterns. In Proceedings of the IEEE 2nd International Conference on Cloud Computing and Big Data Analysis (ICCCBDA) (pp. 344–347).

[26] Martín, A., Menéndez, H. D., Camacho, D., 2017. MOCDroid: Multiobjective evolutionary classifier for Android malware detection. Soft Computing. 21(24), 7405–7415.

[27] Qiu, Y., et al., 2024. A novel image expression-driven modeling strategy for coke quality prediction in the smart cokemaking process. Energy. 294, 130866.

[28] Zhao, F., et al., 2023. A new method using LLMs for keypoints generation in qualitative data analysis. In 2023 IEEE Conference on Artificial Intelligence (CAI). IEEE.

[29] Liu, Y., Yang, H., Wu, C., 2023. Unveiling patterns: a study on semi-supervised classification of strip surface defects. IEEE Access. 11, 119933-119946.

[30] Li, S., et al., 2024. Application of semi-supervised learning in image classification: research on fusion of labeled and unlabeled data. IEEE Access.

[31] Luo, Z., Xu, H., Chen, F., 2019. Audio Sentiment Analysis by Heterogeneous Signal Features Learned from Utterance-Based Parallel Neural Network. AffCon@ AAAI.

[32] Chen, F., Luo, Z., Xu, Y., et al., 2019. Complementary fusion of multi-features and multi-modalities in sentiment analysis. arXiv preprint arXiv:1904.08138.

[33] Luo, Z., Zeng, X, Bao, Z., et al., 2019. Deep learning-based strategy for macromolecules classification with imbalanced data from cellular electron cryotomography. 2019 International Joint Conference on Neural Networks (IJCNN). IEEE.

[34] Luo, Z., 2023. Knowledge-guided aspect-based summarization. 2023 International Conference on Communications, Computing and Artificial Intelligence (CCCAI). IEEE.

[35] Shen, Y., Gu, H.M., Qin, S., Zhang, D.W., 2022. Surf4, cargo trafficking, lipid metabolism, and therapeutic implications. Journal of Molecular Cell Biology. 14(9), mjac063.

[36] Qiu, Y., Chen, H., Dong, X., et al., 2024. IFViT: Interpretable Fixed-Length Representation for Fingerprint Matching via Vision Transformer. arXiv preprint arXiv:2404.08237.

[37] Shen, F., Vecchio, J. D., Mohaisen, A., Ko, S. Y., Ziarek, L., 2019. Android malware detection using complex-flows. IEEE Transactions on Mobile Computing. 18(6), 1231–1245.

[38] Gorla, A., Tavecchia, Ilaria, Gross, Florian, et al., 2014. Checking app behavior against app descriptions. In Proceedings of the 36th International Conference on Software Engineering (ICSE) (pp. 1025–1035).

[39] Li, Y., Y. Ma, M. Chen, et al., 2017. A detecting method for malicious mobile application based on incremental SVM. In Proceedings of the 3rd IEEE International Conference on Computer Communication (ICCC) (pp. 1246–1250).

[40] Deng, X., Oda, S., Kawano, Y., 2023. Graphene-based midinfrared photodetector with bull’s eye plasmonic antenna. Optical Engineering. 62(9), 097102-097102.

[41] Sugaya, T., Deng, X., 2019. Resonant frequency tuning of terahertz plasmonic structures based on solid immersion method. 2019 44th International Conference on Infrared, Millimeter, and Terahertz Waves (IRMMW-THz). IEEE.

[42] Deng, X., Li, L., Enomoto, Mitsuhiro, et al., 2019. Continuously frequency-tuneable plasmonic structures for terahertz bio-sensing and spectroscopy. Scientific reports. 9(1), 3498.

[43] Deng, X., Simanullang, M., Kawano, Y., 2018. Ge-core/a-si-shell nanowire-based field-effect transistor for sensitive terahertz detection. Photonics, 5(2).

[44] Li, S., Singh, Kanupriya, Riedelet, Nathan, et al., 2022. Digital learning experience design and research of a self-paced online course for risk-based inspection of food imports. Food Control. 135, 108698.

[45] Yu, F., Milord, J., Orton, Sarah, et al., 2021. Students' evaluation toward online teaching strategies for engineering courses during COVID. 2021 ASEE Midwest Section Conference.


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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