MMF: A Lightweight Approach of Multimodel Fusion for Malware Detection

Nowadays, the Android system is widely used in mobile devices. The existence of malware in the Android system has posed serious security risks. Therefore, detecting malware has become a main research focus for Android devices. The existing malware detection methods include those based on static analysis, dynamic analysis, and hybrid analysis. The dynamic analysis and hybrid analysis methods require the simulation of malware’s execution in a certain environment, which often incurs high costs. With the aid of contemporary deep learning technology, static method can provide comparably good results without running software. To address these challenges, we propose a novel and efficient multimodel fusion (MMF) malware detection method. MMF innovatively integrates various static features, including application programming interface (API) call characteristics, request permission (RP) features, and bytecode image features. This fusion approach allows MMF to achieve high detection performance without the need for dynamic execution of the software. Compared to existing methods, MMF exhibits a higher accuracy rate of 99.4% and demonstrates superiority over baseline techniques in various metrics. Our comprehensive analysis and experiments confirm MMF’s effectiveness and efficiency in detecting malware, making a significant contribution to the field of Android malware detection.