FLUIDS: Federated Learning with semi-supervised approach for Intrusion Detection System

FLUIDS: Federated Learning with semi-supervised approach for Intrusion Detection System

연구 분야: Artificial Intelligence

학회: 2022 IEEE 19th Annual Consumer Communications & Networking Conference (CCNC)

In this paper, we present FLUIDS, a Federated Learning with semi-sUpervised approach for Intrusion Detection System. FLUIDS formulates the intrusion detection into a semi-supervised learning where both supervised learning (using labeled data) and unsupervised learning (no label data) are combined in a collaborative way. The combination of federated learning and semi-supervised Learning allows the solution to: better preserve the privacy, improve training and inference efficiency, achieve better accuracy, and be cheaper to deploy.