Scholarlink

<p>The Internet of Things (IoT) and Industrial IoT (IIoT) have rapidly evolved, reshaping modern industries through intelligent automation and seamless real-time connectivity. Additionally, the inherent heterogeneity, limited resources, and distributed structure of a network has made them vulnerable. Those issues are used by the cyber attacker to gain an unauthorized access to the system, including data leakage, insider misuse, and Distributed Denial of Service (DDoS) attacks. To counter these security risks, we work to introduce a novel model ASTRID-Net, an innovative deep learning architecture building a Triple attention hybrid model that includes multi-scale convolutional feature extraction, bidirectional recurrent modeling, and Residual learning for high-accuracy intrusion detection in IoT and IIoT networks. The framework integrates multi-scale Convolutional Neural Networks (CNNs) to extract spatial features, Bidirectional Gated Recurrent Units (BiGRUs) to capture temporal relationships, and a combined channel–temporal attention mechanism to prioritize the most relevant information in the data. Experimental evaluation reveals that ASTRID-Net attains an outstanding 99.97% accuracy, with macro-averaged precision, recall, and F1-score surpassing 99.97%, outperforming conventional deep learning baselines. These results confirm the effectiveness and scalability of ASTRID-Net for real-time detection of complex cyber threats in IoT/IIoT infrastructures, contributing to the development of secure and adaptive cyber-physical systems.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

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

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年1月21日

查看全文
  • Ashrafun Zannat,
  • Md. Shakil Ahmmed,
  • Md. Alamgir Hossain,
  • Md. Saiful Islam,
  • Alifa Shanzidah Manarat

摘要

The Internet of Things (IoT) and Industrial IoT (IIoT) have rapidly evolved, reshaping modern industries through intelligent automation and seamless real-time connectivity. Additionally, the inherent heterogeneity, limited resources, and distributed structure of a network has made them vulnerable. Those issues are used by the cyber attacker to gain an unauthorized access to the system, including data leakage, insider misuse, and Distributed Denial of Service (DDoS) attacks. To counter these security risks, we work to introduce a novel model ASTRID-Net, an innovative deep learning architecture building a Triple attention hybrid model that includes multi-scale convolutional feature extraction, bidirectional recurrent modeling, and Residual learning for high-accuracy intrusion detection in IoT and IIoT networks. The framework integrates multi-scale Convolutional Neural Networks (CNNs) to extract spatial features, Bidirectional Gated Recurrent Units (BiGRUs) to capture temporal relationships, and a combined channel–temporal attention mechanism to prioritize the most relevant information in the data. Experimental evaluation reveals that ASTRID-Net attains an outstanding 99.97% accuracy, with macro-averaged precision, recall, and F1-score surpassing 99.97%, outperforming conventional deep learning baselines. These results confirm the effectiveness and scalability of ASTRID-Net for real-time detection of complex cyber threats in IoT/IIoT infrastructures, contributing to the development of secure and adaptive cyber-physical systems.