Deep learning for malware detection systems is an active research area focused on developing intelligent techniques to identify malicious software and cyber threats in real time. Research papers in this domain explore deep learning architectures such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), long short-term memory networks (LSTMs), gated recurrent units (GRUs), autoencoders, and generative adversarial networks (GANs) for static, dynamic, and hybrid malware analysis. Key contributions include feature extraction from binary files, API calls, system logs, network traffic, and behavioral patterns, as well as addressing challenges like obfuscation, polymorphic malware, and large-scale datasets. Recent studies also investigate hybrid models combining deep learning with ensemble learning, reinforcement learning, and graph-based analysis for improved accuracy and robustness. Research further addresses challenges of computational efficiency, real-time detection, adversarial attacks, and deployment in IoT and cloud environments. By leveraging deep learning, malware detection research aims to provide adaptive, accurate, and scalable security solutions to protect systems, networks, and sensitive data from evolving cyber threats.