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This is a Experimental Study study.

September 16, 2025IET Information SecurityOpen Access

Enhancing IoT Security via Federated Learning: A Comprehensive Approach to Intrusion Detection

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This analysis demonstrates that federated learning improves network intrusion detection in IoT, indicating enhanced data privacy and scalability.

Federated learning significantly improves network intrusion detection while preserving data privacy in IoT environments.
Evaluation of various machine learning models revealed that the random forest model provides the highest classification accuracy.
The proposed federated learning approach allows IoT devices to collaboratively train models without sharing raw data.
Experimental results using the UNSW‐NB15 dataset reveal promising outcomes with minimal performance degradation compared to centralized methods.

Bai et al. (2025) studied this question.

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September 16, 2025IET Information SecurityOpen Access

View Full Paper

Ask AI

Bookmark

Authors

This analysis demonstrates that federated learning improves network intrusion detection in IoT, indicating enhanced data privacy and scalability.

Federated learning significantly improves network intrusion detection while preserving data privacy in IoT environments.
Evaluation of various machine learning models revealed that the random forest model provides the highest classification accuracy.
The proposed federated learning approach allows IoT devices to collaboratively train models without sharing raw data.
Experimental results using the UNSW‐NB15 dataset reveal promising outcomes with minimal performance degradation compared to centralized methods.

Bai et al. (2025) studied this question.

View Full Paper

Ask AI

Bookmark