Edge Computing for IoT Security: Integrating Machine Learning with Key Agreement

The increasing prevalence of the Industrial Internet of Things (IIoT) in industrial environments amplifies the potential for security breaches and compromises. To monitor IIoT networks, intrusion detection systems (IDS) have been introduced to detect malicious activities within the network flow, in which machine learning (ML) and deep learning (DL) play an important role. However, existing IDSs face challenges during training when dealing with imbalanced training data and a higher number of classes. These issues can significantly reduce the IDS's performance and may result in missed network attacks, especially those with fewer training samples. To address these challenges, this paper introduces a multi-head attention-based gated recurrent unit (MAGRU) that scrutinizes IIoT network traffic to detect malicious activities. In the proposed model, the multi-head attention (MA) has the ability to enhance the learning capability of the model to handle limited sample classes. The gated recurrent unit (GRU) is employed for the detection of IIoT network behavior. The proposed MAGRU is evaluated using two publicly available datasets, namely Edge-IIoTset and MQTTset. To validate the performance of the proposed MAGRU, various ML and DL models were implemented and compared against MAGRU using the same dataset. The proposed model outperformed the other models, achieving an average precision, recall, F1-score, and accuracy of 99.62%, 99.67%, 99.64%, and 99.97%, respectively, for the aforementioned datasets. These results demonstrate optimal performance in the detection of intrusions in IIoT networks.

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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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Edge Computing for IoT Security: Integrating Machine Learning with Key Agreement

Cited by 4 publications

References 26 publications

Challenges and Solutions in Integrating Narrowband IoT with Edge Computing: Resource Constraints, Security, Latency, and IDS Deployment

Challenges and Solutions in Integrating Narrowband IoT with Edge Computing: Resource Constraints, Security, Latency, and IDS Deployment

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

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

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