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September 28, 2025Open Access

A Robust Model for Android Malware Detection via ML and DL classifiers

Key Points

The model achieved an accuracy of 98.0%, outperforming logistic regression and decision trees in detecting android malware.
Results revealed that the deep learning classifier, an artificial neural network, significantly exceeded performance metrics compared to machine learning methods.
Experimental evaluation utilized the NATICUSdroid dataset, focusing on key metrics like accuracy, precision, recall, and AUC.
The findings emphasize the effectiveness of deep learning approaches in improving the detection of sophisticated android malware threats.

Abstract

The rapid growth of sophisticated Android malware (AM) threats is significant, as Android devices often store private and sensitive personal and financial information. These threats allow stealing of data, interference with device functioning, and network compromise. One of the greatest difficulties in efficient interception systems is ensuring a high level of detection accuracy for distinguishable AM variants. This study focuses on developing a robust Android malware detection model via machine learning (ML) and deep learning (DL) techniques. The model combines ML classifiers, which consist of logistic regression (LR) and decision trees (DTs), and a DL classifier, an artificial neural network (ANN). The model was implemented via an open-source data mining program called Orange. The NATICUSdroid dataset was used to train and test the model, which was measured in terms of accuracy, precision, recall, F-measure and AUC. The experimental findings revealed that the ANN performed the best (accuracy: 98.0%, precision/recall/F-measure: 98.0%, AUC: 0.997) and was better than the LR (accuracy: 96.1%, AUC: 0.989) and DT (accuracy: 96.0%, AUC: 0.971) methods. The results highlight the high potential of DL-based approaches, especially ANNs, to detect Android malware and reinforce their suitability for enhancing mobile security systems.

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