The problem of malicious software (malware) detection and classification is a complex task and there is no perfect approach. There is still a lot of work to be done. Unlike most other research areas, standard benchmarks are difficult to find for malware detection. This paper aims to investigate recent advances in malware detection on MacOS, Windows, iOS, Android, and Linux using deep learning (DL) by answering a few questions: Is it possible to use DL text and image classification, pre-trained, and multi-task learning models for malware detection and get high accuracy? What would be the best approach if we have a standard benchmark dataset? We discuss the issues and the challenges in malware detection using DL classifiers. We believe there is need to train and test the effectiveness and efficiency of the current state-of-the-art deep learning models on different malware datasets. We also examine eight popular DL approaches on various datasets. This survey will help researchers develop a general understanding of malware recognition using deep learning
