Deep learning-based security in the Internet of Things (IoT) is an emerging research area that leverages advanced neural network models to detect, prevent, and mitigate cyber threats in large-scale, heterogeneous IoT environments. Research papers in this domain focus on applying deep learning techniques such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), autoencoders, and generative adversarial networks (GANs) for intrusion detection, malware detection, anomaly detection, and secure authentication in IoT networks. Key contributions include hybrid models combining deep learning with traditional security frameworks, edge/fog-assisted real-time threat detection, and privacy-preserving model training using federated learning. Recent studies also address challenges such as limited computational resources, energy constraints, scalability, and adversarial attacks on deep learning models. By integrating AI-driven analytics with secure communication protocols, blockchain, and zero trust architectures, deep learning-based IoT security research aims to build intelligent, adaptive, and resilient IoT ecosystems.