Federated Learning-Based In-Network Traffic Analysis on IoT Edge

Abstract

The rise of IoT-connected devices has led to an increase in collected data for service and traffic analysis, but also to emerging threats and attacks. In-network machine learning-based attack detection has proven effective in fast response, but scaling to distributed IoT edge devices risks increasing communication overheads and raising data privacy concerns. To address these concerns, we present FLIP4, a distributed in-network attack detection framework based on federated tree models. FLIP4 maintains data privacy by enabling distributed machine learning training while keeping data local on IoT edge, and provides in-network inference within the programmable data plane on edge gateway for timely attack labeling and mitigation. Evaluation results show that FLIP4 can accurately detect attacks while maintaining source data privacy and enabling lightweight deployment on IoT edge.