Jihyeon Song

Hybrid Distributed Learning With Knowledge Distillation for Resource-Efficient Intrusion Detection in Distributed Networks

A novel AI-driven distributed NIDS that considers the computing capabilities of resource-constrained nodes while enabling efficient learning in distributed environments is proposed and can achieve accuracy comparable to a centralized model while reducing local computational overhead and maintaining stable convergence under realistic data distribution scenarios.