Protecting Mobile Communication Channels: Adaptive Super Learner-Based Malware Detection for Android

Abstract

Securing mobile communication channels is increasingly vital due to the growing dependence on Android applications, which are common targets for sophisticated malware. These threats can compromise user privacy, damage system files, and even harm device hardware. The open nature of Android and its large user base amplify its vulnerability. Traditional detection methods often fail to identify new and obfuscated malware variants, such as polymorphic and zero-day attacks. While machine learning offers improvements, many models lack scalability and adaptability to evolving threats. To overcome these challenges, this study introduces an Adaptive Super Learner-Based Malware Detection Model tailored for Android systems. The model combines optimized base learners—Decision Tree (DT), K-Nearest Neighbors (KNN), Multi-Layer Perceptron (MLP), and LightGBM (LGBM)—with Logistic Regression as a meta-learner, enabling dynamic response to emerging malware behaviors. Genetic Algorithm (GA) is employed to select the most relevant features, reducing the dimensionality of the input space while enhancing performance. Evaluated on the Drebin dataset, the model achieved a high accuracy of 99.6% using the full feature set, and 99.3% accuracy after reducing features from 215 to 97—demonstrating strong detection capability, adaptability, and efficiency in securing Android-based communication platforms.