A Hybrid CNN–BiLSTM Framework Optimized with Bayesian Search for Robust Android Malware Detection

With the rapid proliferation of Android smartphones, mobile malware threats have escalated significantly, underscoring the need for more accurate and adaptive detection solutions. This work proposes an innovative deep learning hybrid model that combines Convolutional Neural Networks (CNNs) with Bidirectional Long Short-Term Memory (BiLSTM) networks for learning both local features and sequential behavior in Android applications. To improve the relevance and clarity of the input data, Mutual Information is applied for feature selection, while Bayesian Optimization is adopted to efficiently optimize the model’s parameters. The designed system is tested on standard Android malware datasets and achieves an impressive detection accuracy of 99.3%, clearly outperforming classical approaches such as Support Vector Machines (SVMs), Random Forest, CNN, and Naive Bayes. Moreover, it delivers strong outcomes across critical evaluation metrics like F1-score and ROC-AUC. These findings confirm the framework’s high efficiency, adaptability, and practical applicability, making it a compelling solution for Android malware detection in today’s evolving threat landscape.