Malware Classification Using Convolutional Fuzzy Neural Networks Based on Feature Fusion and the Taguchi Method

Abstract

The applications of computer networks are increasingly extensive, and networks can be remotely controlled and monitored. Cyber hackers can exploit vulnerabilities and steal crucial data or conduct remote surveillance through malicious programs. The frequency of malware attacks is increasing, and malicious programs are constantly being updated. Therefore, more effective malware detection techniques are being developed. In this paper, a convolutional fuzzy neural network (CFNN) based on feature fusion and the Taguchi method is proposed for malware image classification; this network is referred to as FT-CFNN. Four fusion methods are proposed for the FT-CFNN, namely global max pooling fusion, global average pooling fusion, channel global max pooling fusion, and channel global average pooling fusion. Data are fed into this network architecture and then passed through two convolutional layers and two max pooling layers. The feature fusion layer is used to reduce the feature size and integrate the network information. Finally, a fuzzy neural network is used for classification. In addition, the Taguchi method is used to determine optimal parameter combinations to improve classification accuracy. This study used the Malimg dataset to evaluate the accuracy of the proposed classification method. The accuracy values exhibited by the proposed FT-CFNN, proposed CFNN, and original LeNet model in malware family classification were 98.61%, 98.13%, and 96.68%, respectively.