MAGRU-IDS: A Multi-Head Attention-Based Gated Recurrent Unit for Intrusion Detection in IIoT Networks

Ullah, Safi; Boulila, Wadii; Koubâa, Anis; Ahmad, Jawad

doi:10.1109/access.2023.3324657

Cited by 11 publications

(1 citation statement)

References 58 publications

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“…In the context of Industrial Internet of Things (IIoT) applications, GRUs are particularly valuable for handling sequential data and learning long-term dependencies. They have been employed in intrusion detection systems for IIoT networks, such as the MAGRU-IDS model, which utilizes a multi-head attention-based GRU to detect anomalies effectively [130]. Furthermore, GRUs contribute to enhanced security in industrial control systems.…”

Section: Gated Recurrent Unitmentioning

confidence: 99%

Overview of AI-Models and Tools in Embedded IIoT Applications

Dini,

Diana,

Elhanashi

et al. 2024

Electronics

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The integration of Artificial Intelligence (AI) models in Industrial Internet of Things (IIoT) systems has emerged as a pivotal area of research, offering unprecedented opportunities for optimizing industrial processes and enhancing operational efficiency. This article presents a comprehensive review of state-of-the-art AI models applied in IIoT contexts, with a focus on their utilization for fault prediction, process optimization, predictive maintenance, product quality control, cybersecurity, and machine control. Additionally, we examine the software and hardware tools available for integrating AI models into embedded platforms, encompassing solutions such as Vitis AI v3.5, TensorFlow Lite Micro v2.14, STM32Cube.AI v9.0, and others, along with their supported high-level frameworks and hardware devices. By delving into both AI model applications and the tools facilitating their deployment on low-power devices, this review provides a holistic understanding of AI-enabled IIoT systems and their practical implications in industrial settings.

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Section: Gated Recurrent Unitmentioning

confidence: 99%

Overview of AI-Models and Tools in Embedded IIoT Applications

Dini,

Diana,

Elhanashi

et al. 2024

Electronics

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ABCNN-IDS: Attention-Based Convolutional Neural Network for Intrusion Detection in IoT Networks

Momand,

Jan,

Ramzan

2024

Wireless Pers Commun

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This paper proposes an attention-based convolutional neural network (ABCNN) for intrusion detection in the Internet of Things (IoT). The proposed ABCNN employs an attention mechanism that aids in the learning process for low-instance classes. On the other hand, the Convolutional Neural Network (CNN) employed in the ABCNN framework converges toward the most important parameters and effectively detects malicious activities. Furthermore, the mutual information technique is employed during the pre-processing stage to filter out the most significant features from the datasets, thereby improving the effectiveness of the ABCN model. To assess the effectiveness of the ABCNN approach, we utilized the Edge-IoTset, IoTID20, ToN_IoT, and CIC-IDS2017 datasets. The performance of the proposed architecture was assessed using various evaluation metrics, such as precision, recall, F1-score, and accuracy. Additionally, the performance of the proposed model was compared to multiple ML and DL methods to evaluate its effectiveness. The proposed model exhibited impressive performance on all the utilized datasets, achieving an average accuracy of 99.81%. Furthermore, it demonstrated excellent scores for other evaluation metrics, including 98.02% precision, 98.18% recall, and 98.08% F1-score, which outperformed other ML and DL models.

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A Self-Attention-Based Deep Convolutional Neural Networks for IIoT Networks Intrusion Detection

Alshehri,

Saidani,

Alrayes

et al. 2024

IEEE Access

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The Industrial Internet of Things (IIoT) comprises a variety of systems, smart devices, and an extensive range of communication protocols. Hence, these systems face susceptibility to privacy and security challenges, making them prime targets for malicious attacks that can result in harm to the overall system. Privacy breach issues are a notable concern within the realm of IIoT. Various intrusion detection systems based on machine learning (ML) and deep learning (DL) have been introduced to detect malicious activities within these networks and identify attacks. The existing ML and DL-based models face challenges when confronted with highly imbalanced training. Repetitive data in network datasets inflates model performance, as the model has encountered much of the test set data during training. Moreover, these models decrease performance when confronted with datasets that include repetitions of similar data across various classes, where only the class labels are different. To overcome the challenges inherent in existing systems, this paper presents a self-attention-based deep convolutional neural network (SA-DCNN) model designed for monitoring the IIoT networks and detecting malicious activities. Additionally, a two-step cleaning method has been implemented to eliminate redundancy within the training data, considering both intra-class and cross-class samples. The performance of the SA-DCNN model is assessed using IoTID20 and Edge-IIoTset datasets. Furthermore, the proposed study is demonstrated through a comprehensive comparison with other ML and DL models, as well as against relevant studies, showcasing the superior performance and efficacy of the proposed model.

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MAGRU-IDS: A Multi-Head Attention-Based Gated Recurrent Unit for Intrusion Detection in IIoT Networks

Cited by 11 publications

References 58 publications

Overview of AI-Models and Tools in Embedded IIoT Applications

Overview of AI-Models and Tools in Embedded IIoT Applications

ABCNN-IDS: Attention-Based Convolutional Neural Network for Intrusion Detection in IoT Networks

A Self-Attention-Based Deep Convolutional Neural Networks for IIoT Networks Intrusion Detection

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