Research on Machine Learning Assisted Security and Privacy Provisioning for Edge Computing focuses on leveraging machine learning (ML) techniques to detect, prevent, and mitigate security threats and privacy breaches in distributed, resource-constrained edge environments. This area addresses challenges such as heterogeneous edge devices, dynamic network topologies, large-scale IoT data, and real-time processing requirements. Key research directions include ML-based intrusion detection and anomaly detection, predictive threat modeling, and adaptive access control mechanisms for edge nodes. Other emerging topics involve privacy-preserving ML approaches (e.g., federated learning, differential privacy), ML-assisted secure task offloading and data sharing, and intelligent trust management frameworks. Additionally, research on adversarial attack resilience, energy- and latency-aware security provisioning, and ML-driven policy enforcement for edge–cloud collaborative systems represents significant avenues for advancing robust, efficient, and intelligent security and privacy solutions in edge computing infrastructures.