###### Abstract
Although static analysis is fast and safe, it cannot be used to classify unknown malware. Dynamic analysis can be used for accurately identifying the code and determining the functionality of a malicious program; however, this method might affect the application of the computer. Therefore, some researchers have advocated the conversion of malware from binary files to grayscale images and then utilizing various algorithmic techniques to classify these malware images [19]. In contrast to static and dynamic analysis, malware image classification does not require strong malware domain knowledge, and it bypasses the need for malware fine-tuning to overcome obfuscation techniques. Malware image classification thus allows for fast classification applicable to various malware types [20]. Machine learning technology has been widely used to address the malware detection problem. [PERSON] et al. [21] used principal component analysis to extract features, and artificial neural networks (ANN), KNN, and SVM were then used to complete the classification of malware images. [PERSON] and [PERSON] [22] used random forest to classify malware images, and they reported satisfactory classification results. [PERSON] et al. [23] proposed a malware classification framework based on malware visualization and semisupervised learning. This framework mainly includes three parts: the parts for malware visualization, feature extraction, and classification. Feature fusion methods are used to fuse local and global features to save time and improve feature correlation. [PERSON] et al. [24] proposed a classification method based on standard image features. This method is simple and efficient and requires neither disassembly nor code execution. The above-mentioned methods require the user to define the features in advance. In recent years, many scholars have conducted in-depth research with deep learning approaches on malware detection. [PERSON] et al. [25] used LeNet for malware family classification. They used convolution operations to automatically extract malware features. [PERSON] et al. [26] proposed a deep learning architecture to classify malware samples. [PERSON] al. [27] used an adversarial learning framework for unsupervised domain adaptation to enable gradient boosting decision trees to learn domain-invariant features and to mitigate performance degradation in the target domain. Because CNNs have too many parameters and require high-performance hardware, [PERSON] and [PERSON] [28] employed convolution operations with fuzzy neural network to reduce the number of network parameters in breast cancer classification and obtained good performance. Because CNNs require numerous parameters and these parameters are difficult to determine, the trial-and-error method is widely used for parameter selection. To reduce the time and cost of experiments, the Taguchi method [29] can be used to statistically optimize parameter selection by using an orthogonal array of influencing factors and their levels. In this paper, a convolutional fuzzy neural network (CFNN) based on feature fusion and the Taguchi method (FT-CFNN) is proposed for malware image classification. The FT-CFNN comprises two parts: a CFNN and Taguchi-method-based optimal parameter combinations. The CFNN comprises convolutional, pooling, feature fusion, and fuzzy neural network (FNN) layers. Four feature fusion methods, namely global max pooling (GMP) fusion, global average pooling (GAP) fusion, channel global max pooling (CGMP) fusion, and channel global average pooling (CGAP) fusion, are proposed to reduce the feature size and integrate the network information. An FNN is used for classification. In addition, the Taguchi method is used to determine the affecting factors and the best parameter combination for achieving optimal accuracy. In this study, we focus on improving deep learning methods that require a large number of learning parameters and improving classification accuracy by optimizing parameter combinations.