Malware detection is a hotspot of cyberspace security research, such as Windows malware detection and Android malware detection. With the development of machine learning and deep learning, some outstanding algorithms in the fields of image recognition and natural language processing have been applied to malware detection. These algorithms have shown excellent learning performance with a large amount of data. However, there are some challenging problems in malware detection that have not been solved effectively. For instance, conventional learning methods cannot achieve effective detection based on a few novel malware. Therefore, few-shot learning (FSL) is adopted to solve the few-shot for malware detection (FSMD) problems. This study extracts the problem definition and the general process of FSMD by the related research. According to the principle of the method, FSMD methods are divided into methods based on data augmentation, methods based on meta-learning, and hybrid methods combining multiple technologies. Then, the study discusses the characteristics of each FSMD method. Finally, the background, technology, and application prospects of FSMD are proposed.