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Interpretable intrusion detection for IoT environments using a self-attention-based explainable AI framework.

Kanta Prasad Sharma¹, Tapsi Nagpal², Tarak Vora³

¹Computer Science & Engineering, Amity University Greater Noida Campus, Noida, India.

Scientific Reports

|November 14, 2025

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This summary is machine-generated.

This study introduces a novel self-attention deep neural network with learnable feature gating for enhanced Internet of Things (IoT) cybersecurity. The model achieves high accuracy in detecting network intrusions, offering a robust and interpretable solution.

Area of Science:

Cybersecurity
Artificial Intelligence
Network Security

Background:

The proliferation of Internet of Things (IoT) devices presents significant cybersecurity challenges due to expanded attack surfaces.
Existing intrusion detection systems often struggle with the complexity and scale of modern network traffic.

Purpose of the Study:

To propose a novel self-attention deep neural network (SA-DNN) integrated with a learnable feature gating (LFG) mechanism for robust IoT intrusion detection.
To enhance model adaptability and feature optimization without manual feature selection.

Main Methods:

Development of a SA-DNN architecture incorporating an LFG mechanism for dynamic feature emphasis.
Utilizing self-attention to capture long-range dependencies in network traffic.

Keywords:

Cybersecurity in IoT Deep neural networks Explainable artificial intelligence Internet of things IoT intrusion detection Network security Self-attention mechanism

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Employing SHAP and LIME for model interpretability.

Main Results:

Achieved 99.3% accuracy on the BoT-IoT dataset and 99.6% on the N-BaIoT dataset.
Demonstrated strong generalizability with 97.9% accuracy on the UNSW-NB15 dataset.
Outperformed baseline and state-of-the-art methods in intrusion detection.

Conclusions:

The proposed SA-DNN with LFG offers a lightweight, scalable, and interpretable solution for IoT cybersecurity.
The model exhibits robustness and strong generalizability across diverse network intrusion scenarios.
This approach enhances transparency in intrusion detection decisions.