Rising prevalence of malicious software (malware) attacks represent a serious threat to online safety in the modern era. Malware is a threat to anyone who uses the Internet since it steals data and causes damage to computer systems. In addition, the exponential growth of malware hazards that affect many computer users, corporations, and governments has made malware detection, a popular issue in academic study. Current malware detection methods are slow and ineffectual because they rely on static and dynamic analysis of malware signatures and behavior patterns to detect unknown malware in real-time. Thus, this paper discusses the role of deep convolution neural networks in malware classification and solutions for utilizing machine learning to detect and classify malware families through transfer learning. We proposed a CNN pretrained model learning to classify malware families. The experiment was conducted using two classification datasets, including Malimg and ImageNet. We classified the Malimg dataset, which has turned malware binaries into malware images by using Portable Executable. The result shows that the EfficientNet3 model achieved a high accuracy of 99.93%.