Developing a comprehensive methodology for detecting malicious applications on the Android system from start to finish using deep learning

Abstract

The purpose of malware design is to attack computers specifically for data theft operations and system network disruption to gain access to sensitive data. The mobile platform risk increases due to the widespread Android operating system use because its design keeps it open-source by nature. The research demonstrates the use of deep learning composite models for the detection of Android software malware. The proposed method leverages a pre-trained GoogleNet convolutional neural network for feature extraction from malware-related data and employs a deep recurrent BiLSTM network for classification. The MRMR (Minimum Redundancy Maximum Relevance) algorithm enables feature selection in a process that optimizes model classification speed as well as accuracy levels. This detection system addresses three main malware detection obstacles: the analysis of extensive data sets as well as the quick development of complex malware code and its well-camouflaged signatures. The simulation results showed that the model successfully detected various malware types with 99.30% success rate. Research results show that contemporary security threats need complex malware detection systems because conventional antivirus solutions have proven ineffective against new security threats. The constructed AI-based malware detection framework serves as an advanced solution that delivers improved protection for Android devices and their cyber threats.