A hybrid approach for Android malware detection using improved multi-scale convolutional neural networks and residual networks

Xingbing Fu, Chaofan Jiang, Chaorong Li, Jiangtao Li, Xiatian Zhu, Fagen Li

Abstract: Highlights•Propose an improved MSCNN model extracting features from various data levels.•Design hybrid models combining MSCNN with GRU, ResNet18, ResNet34 and ResNet50.•MSCNN acts as upper feature extraction layer.•GRU, ResNet18, ResNet34 and ResNet50 as detection network, respectively.•Our approach detects Android malware, and enhances the hybrid model performance.