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The aim is to create an efficient hybrid framework for detecting Android malware using machine learning and deep learning techniques.

April 13, 2026Open Access

A Hybrid Framework For Real-Time Android Malware Detection Using Machine Learning And Deep Learning

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This research develops a hybrid framework to enhance malware detection in Android applications, suggesting improved security for users.

The aim is to create an efficient hybrid framework for detecting Android malware using machine learning and deep learning techniques.
Integrated real-time data acquisition from Twitter every 48 hours
Analyzed application features and permissions using RNN with LSTM architecture
Combined traditional signature-based methods with intelligent learning mechanisms
Achieved a detection accuracy of approximately 94%
Enhanced detection efficiency through a multi-layer framework
Improved reliability and mobile security for real-time malware detection

Rao et al. (2026) studied this question.

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April 13, 2026Open Access

View Full Paper

Ask AI

Bookmark

Authors

This research develops a hybrid framework to enhance malware detection in Android applications, suggesting improved security for users.

The aim is to create an efficient hybrid framework for detecting Android malware using machine learning and deep learning techniques.
Integrated real-time data acquisition from Twitter every 48 hours
Analyzed application features and permissions using RNN with LSTM architecture
Combined traditional signature-based methods with intelligent learning mechanisms
Achieved a detection accuracy of approximately 94%
Enhanced detection efficiency through a multi-layer framework
Improved reliability and mobile security for real-time malware detection

Rao et al. (2026) studied this question.

View Full Paper

Ask AI

Bookmark