Deep learning-based attack detection for the RPL (Routing Protocol for Low-Power and Lossy Networks) is a focused research area in IoT security, aimed at enhancing the resilience of resource-constrained networks against routing attacks such as rank attacks, sinkhole attacks, wormhole attacks, and selective forwarding. Research papers in this domain explore the use of deep learning models—including convolutional neural networks (CNNs), recurrent neural networks (RNNs), long short-term memory networks (LSTMs), and autoencoders—to analyze network traffic patterns, detect anomalies, and identify malicious nodes in real time. Key contributions include hybrid frameworks that combine deep learning with lightweight intrusion detection systems (IDS), edge/fog-assisted detection for low-latency responses, and privacy-preserving mechanisms suitable for IoT environments. Recent studies also address challenges like limited computational resources, energy efficiency, data imbalance, and adaptability to dynamic network topologies. By leveraging deep learning and advanced analytics, research in this area aims to provide intelligent, automated, and robust security solutions for RPL-based IoT networks.