Abstract
The incidence of malicious threats to computer systems has increased with the increasing use of Android devices and high-speed Internet. Malware visualization mechanism can analyze a computer whenever a software or system crash occurs because of malicious activity. This paper presents a new malware classification approach to recognize such Android device malware families by capturing suspicious processes in the form of different size color images. Important local and global characteristics of color images are extracted through a combined local and global feature descriptor (structure based local and statistical based global combined texture analysis) to reduce the training complexity of neural networks. A multihead ensemble of neural networks is proposed to increase network classification performance by merging prediction results from weak learners (convolutional neural network + gated recurrent unit) and using them as learning input to a multi-layer perceptron meta learner. Two public datasets of Android device malware are used to evaluate the classification and detection performance of the proposed approach. A baseline is established to compare the classification performance of the proposed approach with those of state-of-the-art and previous malware detection approaches. The proposed multihead ensemble improved the malware classification performance, with up to 97.8%, accuracy with the R2-D2 dataset and 94.1% accuracy with the MalNet dataset. The overall results show that a multihead ensemble with multi-step feature extraction is a practical approach to classify and detect Android malware.