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Cross-Layer Federated Learning for Lightweight IoT Intrusion Detection Systems.

Suzan Hajj¹, Joseph Azar², Jacques Bou Abdo³

¹Imagerie et Vision Artificielle (ImVIA) Laboratory, Université de Bourgogne Franche-Comté, 21078 Dijon, France.

Sensors (Basel, Switzerland)

|August 26, 2023

Summary

This study introduces a federated intrusion-detection system (IDS) for IoT security. The lightweight system enhances true-positive rates by 10% through collaborative sampling and anomaly detection, protecting data privacy.

Keywords:

federated learning internet of things lightweight intrusion detection lightweight sampling semi-supervised learning

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Area of Science:

Computer Science
Cybersecurity
Internet of Things (IoT)

Background:

The rapid expansion of Internet of Things (IoT) devices presents significant security and privacy challenges.
Securing IoT networks and their data requires efficient and privacy-preserving solutions, especially for resource-constrained devices.

Purpose of the Study:

To propose a federated sampling and lightweight intrusion-detection system (IDS) for IoT networks.
To enhance IoT security and data privacy using a semi-supervised, K-means-based approach for anomaly detection.

Main Methods:

Federated learning framework for local data processing on IoT devices.
K-means clustering for network traffic sampling and anomaly identification.
Sharing only summary statistics to maintain data privacy.

Main Results:

The proposed federated IDS effectively detects intrusions in IoT networks.
The system demonstrates efficiency suitable for resource-constrained IoT devices.
Collaboration between workers and the central coordinator can increase the true-positive rate by up to 10%.

Conclusions:

The federated IDS offers a viable solution for IoT security and privacy.
The system balances detection performance (precision-recall trade-offs) with privacy preservation.
Collaborative approaches in federated learning can significantly improve intrusion detection accuracy in IoT environments.