## 1 Introduction  
Malware programs are undesirable harmful threats that are intended to damage the security of a computer system. Malware detection has become an essential concern in the cybersecurity community since malware is capable of causing excessive loss and harm to computer security. Every day, a huge amount of malware is generated intentionally. A recent Symantec report in 2019 [1] demonstrated that malware is growing by 36% annually and the total samples of malware are estimated to be beyond 430 million. The rapid growth of malware causes an extensive threat in our daily life. Ransomware locks or encodes the files on a user's device and asks for a payment to restore them, which may lead to large productivity loses [2]. Data breaches due to malware activity often incur huge financial losses for major corporations [3]. Trojans and spyware are used in cyber espionage resulting in damage of geopolitical and international relations [4]. Malware has become a primary concern for computer network [5] and smartphone users [6].  
To counter these threats, deep learning, a technique based on artificial neural networks (ANN), can be employed successfully [7; 8]. Deep learning with its multilayer architecture has a great ability to learn the features of the labeled and unlabeled data. However, when using a deep learning model, we need to train the model on a large data set every time, which takes a lot of time and computational resources. To overcome this learning difficulty, we can use pre-trained deep learning network architectures and extract features to build the classification model [9]. This motivates us to develop pre-trained network-based deep learning architecture for malware class identification and classification.  
In this paper, we utilize binary files of malware as black-and-white (grayscale) images, which are generated using the byte code information and later used for malware classification. Visualizations of malware [10; 11] in such form prove to be efficient in getting a complete view structure of images.  
The conversion of textual data into image form is one of the major problems of malware detection [12]. In parallel, most of the malware detection methods adopt machine learning techniques by analyzing the different features within the malicious codes. These techniques generally experience two major problems: (1) extraction of robust and efficient features effectively for malware detection [13] and (2) data imbalance [14] preventing accurate malware detection and classification. Hence, keeping these problems in the mind, the ultimate challenge is to develop a malware identification model that can address the enormous variations in malware families.  
In this paper, we propose a feature fusion-based solution to classify malware into different classes using features extracted from images as well as from pre-trained deep convolutional networks. The contributions of our proposed method are as follows:  
* The conversion of malware binaries into the grayscale image.
* Data augmentation performed on malware images to overcome the data imbalance within the malware datasets for robust feature extraction to enhance the classifier performance.
* An optimized multimodal feature representation to combine the segmentation-based fractal texture analysis (SFTA) features [15] and deep convolutional neural network (DCNN) features into a single feature vector to obtain a robust malware classification model.  
The other parts of the paper are prepared as follows: Section 2 discusses the related work, whereas Section 3 defines the detail of the proposed methodology. The results and analysis of experimental results and their significance are given in Section 4. Section 5 concludes the paper.