Mist-Assisted Federated Learning for Intrusion Detection in Heterogeneous IoT Networks

TL;DR

This paper introduces a Mist-assisted hierarchical federated learning framework for IoT intrusion detection, effectively handling data heterogeneity and resource constraints to achieve high accuracy and privacy preservation.

Contribution

It proposes a novel multi-layer architecture combining Mist, Edge, Fog, and Cloud to improve federated learning for IoT security in heterogeneous environments.

Findings

Achieves 98-99% accuracy on TON-IoT dataset.

PR-AUC exceeds 0.97, indicating strong detection performance.

Demonstrates stable convergence and efficiency under diverse conditions.

Abstract

The rapid growth of the Internet of Things (IoT) offers new opportunities but also expands the attack surface of distributed, resource-limited devices. Intrusion detection in such environments is difficult due to data heterogeneity from diverse sensing modalities and the non-IID distribution of samples across clients. Federated Learning (FL) provides a privacy-preserving alternative to centralized training, yet conventional frameworks struggle under these conditions. To address this, we propose a Mist-assisted hierarchical framework for IoT intrusion detection. The architecture spans four layers: (i) Mist, where raw data are abstracted into a unified feature space and lightweight models detect anomalies; (ii) Edge, which applies utility-based client selection; (iii) Fog, where multiple regional aggregators use FedProx to stabilize training; and (iv) Cloud, which consolidates and disseminates global models. Evaluations on the TON-IoT dataset show the framework achieves 98-99% accuracy, PR-AUC> 0.97, and stable convergence under heterogeneous and large-scale settings, while maintaining efficiency and preserving privacy.