  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 investigating DL in text and image classification, the use of
pre-trained and multi-task learning models for malware detection approaches to
obtain high accuracy and which the best approach if we have a standard
benchmark dataset. We discuss the issues and the challenges in malware
detection using DL classifiers by reviewing the effectiveness of these DL
classifiers and their inability to explain their decisions and actions to DL
developers presenting the need to use Explainable Machine Learning (XAI) or
Interpretable Machine Learning (IML) programs. Additionally, we discuss the
impact of adversarial attacks on deep learning models, negatively affecting
their generalization capabilities and resulting in poor performance on unseen
data. We believe there is a need to train and test the effectiveness and
efficiency of the current state-of-the-art deep learning models on different
malware datasets. We examine eight popular DL approaches on various datasets.
This survey will help researchers develop a general understanding of malware
recognition using deep learning.
