Android Malware Detection and Classification with Analysing Permission API’s using Recurrent Neural Network

• DOI: 10.52783/jisem.v10i10s.1408
• Publication Date: 2025-02-19T11:29:41Z
• Authors: Vijay Sonawane

Android applications often contain vulnerabilities that can be exploited by malicious software, making automated malware detection is crucial. Since software vulnerabilities can have severe repercussions, a great deal of research and development effort has been put into developing mitigation strategies. Over the past few years, a number of different approaches have been tried in an effort to lessen the danger posed by vulnerabilities in software. The proposed approach involves multiple stages, including APK recompilation, feature extraction from manifest permissions, model training, testing, and performance analysis. The process begins with APK recompilation, where Android package files are decompiled using specialized tools to extract critical components such as manifest.xml, which contains permissions requested by the application. These permissions serve as key indicators of potential malicious intent. The extracted XML-based permissions and additional metadata are then transformed into structured feature vectors. Feature selection techniques are applied to retain the most relevant attributes, reducing noise and enhancing classification accuracy. The core classification module leverages Recurrent Neural Networks (RNNs) to analyze the extracted features and identify malicious patterns. The model undergoes supervised training using a labeled dataset comprising both benign and malicious APKs. During training, the RNN learns temporal dependencies and feature interactions, improving its ability to detect sophisticated malware. Once trained, the model is tested on a separate dataset to evaluate its classification performance. Various evaluation metrics such as accuracy, precision, recall, and F1-score are used to measure effectiveness. Finally the proposed model is compared with traditional machine learning classifiers and existing deep learning techniques. The results demonstrate that RNN-based classification significantly enhances malware detection accuracy due to its ability to process sequential patterns within application permissions. This research contributes to the field of Android security by presenting an efficient, scalable, and automated malware detection framework, leveraging deep learning for real-time classification of malicious applications.