This paper provides an in-depth survey of recent advancements in malware detection using deep learning (DL) on various operating systems such as MacOS, Windows, iOS, Android, and Linux. It discusses the challenges and issues in malware detection, including the lack of standard benchmarks and the need for explainable machine learning (XAI) or interpretable machine learning (IML) to understand the decisions made by DL classifiers. The paper also explores the impact of adversarial attacks on deep learning models and the importance of training and testing these models on diverse malware datasets. It reviews eight popular DL approaches and their effectiveness on different datasets. The contributions of the paper include a comprehensive overview of malware attacks, an analysis of malware detection methods, and a detailed examination of data generation, image classification, feature reduction, transfer learning, and natural language processing (NLP) techniques for malware detection. The paper concludes with a discussion on the application of deep learning in cryptographic ransomware detection and the importance of XAI for improving the trustworthiness and reliability of malware detection models.This paper provides an in-depth survey of recent advancements in malware detection using deep learning (DL) on various operating systems such as MacOS, Windows, iOS, Android, and Linux. It discusses the challenges and issues in malware detection, including the lack of standard benchmarks and the need for explainable machine learning (XAI) or interpretable machine learning (IML) to understand the decisions made by DL classifiers. The paper also explores the impact of adversarial attacks on deep learning models and the importance of training and testing these models on diverse malware datasets. It reviews eight popular DL approaches and their effectiveness on different datasets. The contributions of the paper include a comprehensive overview of malware attacks, an analysis of malware detection methods, and a detailed examination of data generation, image classification, feature reduction, transfer learning, and natural language processing (NLP) techniques for malware detection. The paper concludes with a discussion on the application of deep learning in cryptographic ransomware detection and the importance of XAI for improving the trustworthiness and reliability of malware detection models.