Deep learning models for DDoS (Distributed Denial-of-Service) attack detection in IoT networks constitute a significant research area aimed at protecting large-scale, heterogeneous IoT deployments from volumetric and application-layer attacks. Research papers in this domain explore the application of models such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), long short-term memory networks (LSTMs), autoencoders, and hybrid architectures to identify abnormal traffic patterns, malicious payloads, and flooding behaviors in real time. Key contributions include lightweight and energy-efficient algorithms suitable for resource-constrained IoT devices, edge/fog-assisted detection frameworks for low-latency mitigation, and adaptive models capable of learning evolving attack strategies. Recent studies also investigate challenges such as data imbalance, high-dimensional traffic features, scalability, and adversarial attacks on deep learning models. By combining AI-driven analytics with secure communication and network monitoring, deep learning-based DDoS detection research aims to provide intelligent, robust, and proactive security solutions for resilient IoT networks.