A HYBRID ENSEMBLE LEARNING APPROACH FOR INTELLIGENT ANDROID MALWARE DETECTION

Authors

  • K Samson Paul, S. Manoj Kumar, G. Loknath, C. Gnanesh

Keywords:

.

Abstract

The rapid increase in Android malware attacks poses significant security threats to mobile users, leading to data breaches,
financial fraud, and unauthorized access. Traditional malware detection methods, including signature-based and heuristic
approaches, often fail to identify zero-day threats and evolving malware variants. To address these limitations, this study proposes a Hybrid Ensemble Learning Approach for Intelligent Android Malware Detection, combining multiple machine learning models to enhance detection accuracy and robustness. The proposed system integrates static and dynamic analysis techniques to extract comprehensive feature representations of Android applications.

References

H. Rathore, A. Nandanwar, S. K. Sahay, and M. Sewak, ‘‘Adversarial superiority in Android malware detection: Lessons from reinforcement learning based evasion attacks and defenses,’’ Forensic Sci. Int., Digit. Invest., vol. 44, Mar. 2023, Art. no. 301511.

Downloads

Published

2024-09-10

How to Cite

K Samson Paul, S. Manoj Kumar, G. Loknath, C. Gnanesh. (2024). A HYBRID ENSEMBLE LEARNING APPROACH FOR INTELLIGENT ANDROID MALWARE DETECTION. Journal of Computational Analysis and Applications (JoCAAA), 33(4), 612–620. Retrieved from https://eudoxuspress.com/index.php/pub/article/view/2004

Issue

Vol. 33 No. 4 (2024): Selected Papers on Computational Techniques Across Multidisciplinary Fields: Innovations and Applications

Section

Articles

License

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.