Intelligent Hyperparameter-Tuned Deep Learning-Based Android Malware Detection and Classification Model

Abstract

Recently, Android applications have been playing a vital part in the everyday life as several services are offered via mobile applications. Due of its market dominance, Android is more at danger from malicious software, and this threat is growing. The exponential growth of malicious Android apps has made it essential to develop cutting-edge methods for identifying them. Despite the prevalence of a number of security-based approaches in the research, feature selection (FS) methods for Android malware detection methods still have to be developed. In this research, researchers provide a method for distinguishing malicious Android apps from legitimate ones by using a intelligent hyperparameter tuned deep learning based malware detection (IHPT-DLMD). Extraction of features and preliminary data processing are the main functions of the IHPT-DLMD method. The proposed IHPT-DLMD technique initially aims to determine the considerable permissions and API calls using the binary coyote optimization algorithm (BCOA)-based FS technique, which aids to remove the unnecessary features. Besides, bidirectional long short-term memory (Bi-LSTM) model is employed for the detection and classification of Android malware. Finally, the glowworm swarm optimization (GSO) algorithm is applied to optimize the hyperparameters of the BiLSTM model to produce effectual outcomes for Android application classification. This IHPT-DLMD method is checked for quality using a benchmark dataset and evaluated in several ways. The test data demonstrated overall higher performance of the IHPT-DLMD methodology in comparison to the most contemporary methods that are currently in use.