In-network Attack Detection with Federated Deep Learning in IoT Networks: Real Implementation and Analysis

TL;DR

This paper presents a federated deep learning approach using lightweight autoencoders for real-time, privacy-preserving attack detection in resource-constrained IoT networks, validated on a Raspberry Pi testbed.

Contribution

It introduces a novel federated autoencoder-based anomaly detection framework tailored for IoT edge devices, combining real-world implementation with performance analysis.

Findings

Effective attack detection with high accuracy

Reduced communication overhead in federated setup

Comparable performance to centralized methods

Abstract

The rapid expansion of the Internet of Things (IoT) and its integration with backbone networks have heightened the risk of security breaches. Traditional centralized approaches to anomaly detection, which require transferring large volumes of data to central servers, suffer from privacy, scalability, and latency limitations. This paper proposes a lightweight autoencoder-based anomaly detection framework designed for deployment on resource-constrained edge devices, enabling real-time detection while minimizing data transfer and preserving privacy. Federated learning is employed to train models collaboratively across distributed devices, where local training occurs on edge nodes and only model weights are aggregated at a central server. A real-world IoT testbed using Raspberry Pi sensor nodes was developed to collect normal and attack traffic data. The proposed federated anomaly detection system, implemented and evaluated on the testbed, demonstrates its effectiveness in accurately identifying network attacks. The communication overhead was reduced significantly while achieving comparable performance to the centralized method.