Intelligent Android Malware Classification Using Equilibrium Optimizer and Deep LearningModel

Abstract

The rapid growth of Android applications has significantly increased the risk of malware attacks, making reliable malware detection an important cybersecurity challenge. Traditional detection methods often fail to identify newly emerging malware due to their dependence on predefined signatures and limited feature learning capabilities. This paper presents an intelligent Android malware detection framework that combines deep learning with the Equilibrium Optimizer to improve detection performance. Initially, Android application data are preprocessed to extract meaningful features for analysis. A Channel Attention Long ShortTerm Memory (CA-LSTM) model is employed to capture complex behavioral patterns and accurately distinguish malicious applications from benign ones. To further enhance the model's performance, the Equilibrium Optimizer is used to tune the hyperparameters, resulting in improved classification accuracy and faster convergence. Experimental evaluation on a benchmark Android malware dataset demonstrates that the proposed framework achieves superior detection performance compared with conventional machine learning methods, providing an effective and reliable solution for securing Android devices against evolving malware threats.