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ASTRID-Net: SE-enhanced triple attention deep learning framework for IoT and IIoT security.

Ashrafun Zannat1, Md Shakil Ahmmed2, Md Alamgir Hossain3

  • 1Department of Computer Science and Engineering, Bangladesh Army University of Science and Technology, Saidpur, Nilphamari, 5400, Bangladesh. spzannat@baust.edu.bd.

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Summary

ASTRID-Net, a novel deep learning model, achieves over 99.97% accuracy for detecting cyber threats in Internet of Things (IoT) and Industrial IoT (IIoT) networks. This advanced system enhances security for connected environments.

Keywords:
Bidirectional gated recurrent unit (BiGRU)Cyber-Physical systems securityEdge-IIoTset datasetMulti-Scale convolutional neural networksMulti-scale convolutional neural network (CNN)Squeeze-and-Excitation (SE) block)Triple attention

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Area of Science:

  • Cybersecurity and Network Engineering
  • Artificial Intelligence and Machine Learning
  • Internet of Things (IoT) and Industrial IoT (IIoT) Security

Background:

  • IoT and IIoT networks face significant security vulnerabilities due to their distributed, heterogeneous, and resource-limited nature.
  • Cyber attackers exploit these weaknesses for unauthorized access, data leakage, insider misuse, and Distributed Denial of Service (DDoS) attacks.
  • Existing security measures struggle to cope with the complexity and scale of modern IoT/IIoT threats.

Purpose of the Study:

  • To introduce ASTRID-Net, a novel deep learning architecture for high-accuracy intrusion detection in IoT and IIoT environments.
  • To develop a robust framework capable of identifying and mitigating sophisticated cyber threats in real-time.
  • To enhance the security and adaptability of cyber-physical systems.

Main Methods:

  • ASTRID-Net employs a Triple Attention Hybrid Model integrating multi-scale Convolutional Neural Networks (CNNs) for spatial feature extraction.
  • Bidirectional Gated Recurrent Units (BiGRUs) are utilized to capture temporal dependencies within network data.
  • A combined channel-temporal attention mechanism prioritizes critical information for improved detection accuracy.

Main Results:

  • ASTRID-Net achieved an outstanding accuracy of 99.97% in experimental evaluations.
  • Macro-averaged precision, recall, and F1-score exceeded 99.97%, demonstrating superior performance.
  • The model significantly outperformed conventional deep learning baselines in intrusion detection tasks.

Conclusions:

  • ASTRID-Net proves highly effective and scalable for real-time detection of complex cyber threats in IoT/IIoT infrastructures.
  • The model's advanced architecture provides robust security against diverse cyber attacks.
  • Findings support the development of more secure and adaptive cyber-physical systems.