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This paper presents a two-stage machine learning approach to enhance the sustainability and accuracy of malware detection in Android applications. The method involves predicting the release year of an app and classifying it as benign or malicious using a weighted voting mechanism on year-specific models. Experimental results indicate that this approach balances accuracy and computational efficiency, making it a viable solution for real-world malware detection challenges.

Author

Ravi Maganti

Language

EN