International Journal of Computer Techniques Volume 12 Issue 3 | Advanced Malware Detection: Leveraging Hybrid Machine Learning and Deep Learning Models on App Metadata
Advanced Malware Detection: Leveraging Hybrid Machine Learning and Deep Learning Models on App Metadata
Perka Abhilasha, P Suresh Kumar, Nyalakanti Abhishek, G Baskera Vamshi | International Journal of Computer Techniques | Volume 12 Issue 3
Abstract
As mobile applications become more widespread, the risk of malware threats has escalated. This research integrates hybrid machine learning and deep learning techniques to improve malware detection accuracy…
Keywords
Malware detection, Machine learning, Deep learning, App metadata, SVM, Decision Trees, Logistic Regression
Conclusion
The integration of machine learning and deep learning improves malware detection performance, with future enhancements aimed at real-time threat analysis and optimization…
References
- A. O. Christiana, B. A. Gyunka, and A. Noah, “Android Malware Detection through Machine Learning Techniques,” International Journal of Online Biomedical Engineering, 2020.
- J. Li, et al., “Significant Permission Identification for Machine-Learning-Based Android Malware Detection,” IEEE Transactions on Industrial Informatics, 2018.
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