Deep learning-based intelligent intrusion detection for the Internet of Things (IoT) is a vital research area focused on developing advanced security mechanisms to detect and mitigate cyber threats in heterogeneous, resource-constrained IoT environments. Research papers in this domain explore the use of deep learning models such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), long short-term memory networks (LSTMs), autoencoders, and hybrid architectures for real-time anomaly detection, malware detection, and intrusion identification. Key contributions include lightweight, energy-efficient algorithms suitable for IoT devices, edge/fog-assisted intrusion detection frameworks for low-latency response, and privacy-preserving methods using federated learning. Recent studies also address challenges such as imbalanced datasets, adversarial attacks, dynamic network topologies, and scalability for large IoT deployments. By combining AI-driven analytics with secure communication protocols, deep learning-based intelligent intrusion detection research aims to build autonomous, adaptive, and resilient IoT ecosystems capable of defending against evolving security threats.