## 1 Introduction  
Security is the body of procedures, methods, techniques, and technologies that come together to defend the availability, confidentiality, and integrity of information from intrusion. The information is protected by a variety of defense mechanisms, including firewalls, antivirus software systems, and others. Nevertheless, many attackers continue to attempt to log into any system by identifying a single vulnerability in the system that require protection [1]. Software programs known as malware (malicious software) are intended to harm or carry out any kind of undesired activity on a computer system, such as interfering with computer operations, gathering confidential details, evading security controls, and displaying undesirable ads. Massive amounts of malware are purposefully produced every day. The market for malicious software has grown to be more expensive and is dynamically expanding yearly. Based on how it operates, Figure 1 illustrates the various classes of malware, including (adware, spyware, virus, trojan, worm, and backdoor, among others) [2, 3]. The previous methods are unable to guard against the rise in malware attacks, which led numerous researchers to suggest various ways for classifying malware. Figure 2 shows a general taxonomy of malware classification (MC) across several domain types. As illustrated in Figure 3, most current MC approaches can be divided into two categories: hand-crafted and deep learning-crafted. Figure 1: Malware Classes [3]. Figure 3: Malware classification techniques across a variety of features kinds. Figure 2: Malware Classification Taxonomy. Hand-crafted features are a common feature of conventional MC techniques. The manual selection of the feature type is part of the feature extraction process. While the deep learning methods can identify and classify malware automatically. With the increase in malware, earlier works based on conventional techniques are time-consuming and ineffective. The visualization technique is efficient in terms of computing speed and time. The raw pixel data from photos cannot be handled by traditional machine learning techniques; therefore, increased earning is not possible. Raw data must be converted into feature vectors, which requires a high level of engineering and technical expertise. The converted form of the input photos is trained by the classification model. Deep learning approaches allow automatic learning by enabling representation learning to employ unprocessed input data [4]. Convolutional Neural Networks (CNNs), which are employed in image classification, are among the most well-known deep learning techniques. Because CNN offers a better representation of the data, manual feature design can be ignored. The image data is first transformed into a pixel array. The pixel array is then analyzed by a number of convolutional layers, which ultimately produce an expected output. Largest and most well-known datasets, including ImageNet, are used to train the CNN models. However, the information learned from CNNs can be transferred to various learning tasks to improve malware classification and detection. The main benefit of transfer learning would be that it permits task training with a small dataset by employing a model that has already been trained on a big dataset. Most of the current MC methods generally have numerous serious problems: (1) Finding accurate and convenient features for malware classification, (2) the creation of the features requires extensive feature engineering or highly specialized domain expertise, and (3) a poor rate of classification accuracy, and (4) an unbalance in the data that makes it difficult to classify and accurately identify malware.