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MOAT: Federated Learning-Based Method for Improved Detection of IoT Botnets

Yang Zhang,Wenjian Liu,Tianqing Zhu

Published 2026 in IEEE Internet of Things Journal

ABSTRACT

This research introduces a novel intrusion detection method that examines network traffic from Internet of Things (IoT) devices. Due to their constrained computational resources, IoT devices are inherently more vulnerable to cyber-attacks compared to traditional computing systems. Botnets, which often orchestrate distributed denial-of-service (DDoS) attacks by leveraging numerous IoT devices, pose a significant security threat. Consequently, developing effective strategies to identify and mitigate botnet impacts within IoT environments is crucial. Our approach employs an Internet protocol (IP) and port-based classification model capable of recognizing previously unseen intrusion types post-deployment. By monitoring changes in device behavior, the system effectively distinguishes between normal and anomalous activities. We demonstrate the efficacy of our method by targeting two prominent IoT botnets: Mirai and Bashlite. Additionally, we evaluate the benefits of integrating bootstrapping with averaging techniques during the preprocessing phase and find that this combination significantly enhances the model’s generalizability. The model optimization and aggregation technique (MOAT) framework exhibits outstanding performance in both localized and federated intrusion detection scenarios, achieving an average accuracy of 96.25% across nodes, even when tested against attack types present in the training data.

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