Research on Deep Learning-based Security in Edge Computing focuses on leveraging deep neural networks to detect, prevent, and mitigate security threats in distributed, resource-constrained edge environments. This area addresses challenges such as heterogeneous edge devices, dynamic workloads, real-time threat detection, and large-scale data streams from IoT and mobile applications. Key research directions include deep learning-based intrusion detection and anomaly detection, malware and ransomware identification, and predictive threat modeling for proactive defense. Other emerging topics involve federated and distributed deep learning for privacy-preserving security, lightweight and energy-efficient deep learning models for edge deployment, and adversarial attack resilience. Additionally, research on AI-driven access control, secure task offloading, anomaly-based behavior analysis, and edge–cloud collaborative security frameworks represents significant avenues for advancing intelligent, adaptive, and robust security solutions in edge computing systems.