Deep learning-based security in edge computing has become a pivotal research area, focusing on leveraging deep neural networks and AI techniques to detect, prevent, and respond to cyber threats in resource-constrained and distributed edge environments. Research papers in this domain explore applications such as intrusion detection, malware and ransomware detection, anomaly detection, phishing detection, and secure authentication for IoT devices, autonomous systems, and industrial edge networks. Studies emphasize designing lightweight and adaptive deep learning models that can operate efficiently on edge nodes with limited computational and energy resources. Recent works investigate hybrid approaches combining edge and cloud intelligence, federated learning for privacy-preserving threat detection, and adversarial training to improve model robustness against sophisticated attacks. Additionally, security-aware edge frameworks integrate deep learning with blockchain, Zero Trust Architectures, and context-aware mechanisms to enhance trust, resilience, and real-time threat mitigation. Overall, deep learning-based security in edge computing enables intelligent, proactive, and scalable protection for next-generation cyber-physical systems, balancing high detection accuracy with low latency and resource efficiency.