Research topics in deep learning for anomaly detection focus on developing advanced models that can effectively identify rare or abnormal patterns in complex, high-dimensional data across domains such as cybersecurity, healthcare, industrial systems, finance, and IoT networks. Current directions include designing robust unsupervised and semi-supervised learning techniques to overcome the scarcity of labeled anomaly data, leveraging autoencoders, GANs, and self-supervised methods for feature extraction, and applying graph neural networks for relational anomaly detection in interconnected systems. Other promising areas involve explainable anomaly detection to enhance model transparency, federated learning frameworks for privacy-preserving anomaly detection across distributed environments, real-time detection in streaming data, and the integration of multimodal data sources to capture diverse anomaly signatures. Furthermore, researchers are exploring energy-efficient deep models for deployment on edge and resource-constrained devices, adversarial robustness to prevent evasion attacks, and hybrid approaches that combine statistical, rule-based, and deep learning methods for improved accuracy and interpretability.