This paper presents a survey of malware detection using deep learning (DL), focusing on the application of DL techniques in text and image classification for malware detection. The authors discuss various DL approaches, including convolutional neural networks (CNNs), generative adversarial networks (GANs), and multi-task learning models, for malware detection on different operating systems such as Windows, macOS, iOS, Android, and Linux. They also examine the challenges and limitations of DL in malware detection, including the inability of DL models to explain their decisions and the impact of adversarial attacks on DL models. The paper highlights the importance of using explainable machine learning (XAI) or interpretable machine learning (IML) to improve the trustworthiness and reliability of DL models in malware detection. The authors also discuss the use of natural language processing (NLP) for malware classification, the application of transfer learning in malware detection, and the use of deep learning for cryptographic ransomware detection. The paper concludes that DL models, particularly CNN-based models, have shown promising results in malware detection, but further research is needed to improve their accuracy, efficiency, and robustness. The authors also emphasize the importance of using large and diverse malware datasets for training and testing DL models to ensure their effectiveness in real-world scenarios.This paper presents a survey of malware detection using deep learning (DL), focusing on the application of DL techniques in text and image classification for malware detection. The authors discuss various DL approaches, including convolutional neural networks (CNNs), generative adversarial networks (GANs), and multi-task learning models, for malware detection on different operating systems such as Windows, macOS, iOS, Android, and Linux. They also examine the challenges and limitations of DL in malware detection, including the inability of DL models to explain their decisions and the impact of adversarial attacks on DL models. The paper highlights the importance of using explainable machine learning (XAI) or interpretable machine learning (IML) to improve the trustworthiness and reliability of DL models in malware detection. The authors also discuss the use of natural language processing (NLP) for malware classification, the application of transfer learning in malware detection, and the use of deep learning for cryptographic ransomware detection. The paper concludes that DL models, particularly CNN-based models, have shown promising results in malware detection, but further research is needed to improve their accuracy, efficiency, and robustness. The authors also emphasize the importance of using large and diverse malware datasets for training and testing DL models to ensure their effectiveness in real-world scenarios.