Deep learning-based security in the Internet of Things (IoT) is an extensive research domain that leverages deep neural architectures to address the unique security challenges of IoT ecosystems, which are highly heterogeneous, resource-constrained, and vulnerable to diverse cyberattacks. Traditional security mechanisms often fail in IoT due to scalability issues, dynamic environments, and massive device connectivity, whereas deep learning models offer automated feature learning, anomaly detection, and real-time adaptive security solutions. Research explores convolutional neural networks (CNNs) for traffic classification, recurrent neural networks (RNNs) and long short-term memory (LSTM) models for sequential attack detection, autoencoders for anomaly-based intrusion detection, and graph neural networks (GNNs) for capturing device-to-device relationships in IoT networks. Applications include intrusion detection, malware detection, secure authentication, privacy-preserving communication, and detection of denial-of-service (DoS/DDoS), routing, and spoofing attacks. Recent advancements investigate lightweight and energy-efficient deep models for constrained IoT devices, federated learning for privacy-preserving collaborative security, adversarial robustness against evasion attacks, and hybrid architectures combining deep learning with traditional security protocols. These developments position deep learning as a critical enabler for intelligent, scalable, and resilient IoT security frameworks.