Siragoni Sai Divya, Dr A Yashwanth Reddy 03 05 The rapid growth of Android applications has led to a significant increase in malware threats posing serious risks to user privacy data security and device integrity Traditional malware detection techniques often struggle to identify sophisticated and evolving threats due to their reliance on signaturebased or static analysis methods To address these challenges this paper proposes a deep learningdriven pattern recognition framework integrated with an equilibrium optimizer for efficient Android malware detection The proposed approach combines the feature extraction capabilities of deep learning models with the optimization strength of the equilibrium optimizer to enhance detection accuracy and model performance The system begins by extracting relevant features from Android application packages APKs including permissions API calls and behavioral characteristics These features are then processed using a deep learning model such as a convolutional or recurrent neural network to learn complex patterns associated with malicious and benign applications The equilibrium optimizer is employed to finetune model parameters and optimize feature selection reducing redundancy and improving classification efficiency Experimental results demonstrate that the proposed hybrid model achieves high detection accuracy precision and recall compared to traditional machine learning approaches The integration of optimization techniques significantly enhances model convergence and reduces computational complexity Additionally the system shows strong robustness against newly emerging malware variants making it suitable for realworld deployment Overall this work provides an effective and scalable solution for Android malware detection by leveraging the synergy between deep learning and metaheuristic optimization contributing to improved mobile security and threat mitigation Copyright (c) Journal of Science Engineering Technology and Management Science. All rights reserved 2026