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Improving IoT security through an explainable hybrid CNN-transformer model and federated learning.

Aymen M Al-Hejri^1,2, Riyadh M Al-Tam³, Archana Harsing Sable⁴

¹Faculty of Administrative and Computer Sciences, University of Albaydha, Albaydha, Yemen. aymen.muslih.alhejri@ar-rasheed.edu.ye.

Scientific Reports

|May 27, 2026

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This study introduces a hybrid CNN-Transformer model with federated learning for privacy-preserving intrusion detection in Internet of Things (IoT) environments. It enhances cybersecurity by accurately identifying threats while maintaining data privacy.

Area of Science:

Cybersecurity
Artificial Intelligence
Network Security

Background:

Internet of Things (IoT) devices are rapidly increasing, leading to more sophisticated cybersecurity threats against critical infrastructure.
Current intrusion detection systems (IDS) face challenges with data privacy in centralized models or limited pattern recognition in single-architecture systems.
Existing methods struggle to simultaneously analyze local spatial patterns and long-range temporal dependencies in network traffic.

Purpose of the Study:

To propose a novel, explainable, hybrid Convolutional Neural Network (CNN)-Transformer model integrated with federated learning (FL) for privacy-preserving intrusion detection in IoT.
To address the limitations of existing IDS by enhancing both detection accuracy and data privacy.
To improve the interpretability and trustworthiness of automated IoT intrusion detection systems.

Keywords:

CNN-transformer Centralized learning Explainable AI (XAI)Federated learning Intrusion detection IoT security

Related Experiment Videos

Main Methods:

Developed a dual-block CNN-Transformer architecture for comprehensive network traffic analysis.
Integrated federated learning (FL) with FedAvg aggregation for privacy-preserving collaborative model training.
Incorporated Local Interpretable Model-Agnostic Explanations (LIME) for transparent, feature-level insights into detection decisions.
Evaluated the model on the IoT-23 dataset for both binary and multi-class attack classification.

Main Results:

Achieved 94.89% accuracy in federated binary classification and 92.17% in federated multi-class classification on the IoT-23 dataset.
Significantly outperformed standalone CNN and ensemble baseline models in intrusion detection tasks.
Demonstrated model generalizability through an ablation study on the CIC IoT-DIAD 2024 dataset.
LIME integration provided actionable explanations for real-time security analysis.

Conclusions:

The proposed hybrid CNN-Transformer model with federated learning offers a robust and privacy-preserving solution for IoT intrusion detection.
The integration of explainability features (LIME) enhances the trustworthiness and practical utility of the system for security analysts.
This framework effectively addresses the limitations of traditional IDS, improving both detection performance and data privacy in complex IoT environments.