Novel Online Network Intrusion Detection System for Industrial IoT Based on OI-SVDD and AS-ELM

Gyamfi, Eric; Jurcut, Anca Delia

doi:10.1109/jiot.2022.3172393

Cited by 37 publications

(14 citation statements)

References 36 publications

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“…In contrast, Deep Neural Network (DNN) was employed upon supervised learning to generate high-level data representation in unlabelled and noisy data. Gyamfi et al [19] presented a lightweight IDS based on the Online Incremental Support Vector Data Description (OI-SVDD) anomaly detection method on IIoT devices and Adaptive Sequential Extreme Learning Machine (AS-ELM) on Multi-access Edge Computing (MEC) servers. Furthermore, the authors employed MEC servers that offered computational resources to execute the AS-ELM technique at network edges.…”

Section: Literature Reviewmentioning

confidence: 99%

Optimal Deep Learning Based Intruder Identification in Industrial Internet of Things Environment

Alalayah¹,

Alrayes²,

Alaidarous³

et al. 2023

Computer Systems Science and Engineering

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With the increased advancements of smart industries, cybersecurity has become a vital growth factor in the success of industrial transformation. The Industrial Internet of Things (IIoT) or Industry 4.0 has revolutionized the concepts of manufacturing and production altogether. In industry 4.0, powerful Intrusion Detection Systems (IDS) play a significant role in ensuring network security. Though various intrusion detection techniques have been developed so far, it is challenging to protect the intricate data of networks. This is because conventional Machine Learning (ML) approaches are inadequate and insufficient to address the demands of dynamic IIoT networks. Further, the existing Deep Learning (DL) can be employed to identify anonymous intrusions. Therefore, the current study proposes a Hunger Games Search Optimization with Deep Learning-Driven Intrusion Detection (HGSODL-ID) model for the IIoT environment. The presented HGSODL-ID model exploits the linear normalization approach to transform the input data into a useful format. The HGSO algorithm is employed for Feature Selection (HGSO-FS) to reduce the curse of dimensionality. Moreover, Sparrow Search Optimization (SSO) is utilized with a Graph Convolutional Network (GCN) to classify and identify intrusions in the network. Finally, the SSO technique is exploited to fine-tune the hyper-parameters involved in the GCN model. The proposed HGSODL-ID model was experimentally validated using a benchmark dataset, and the results confirmed the superiority of the proposed HGSODL-ID method over recent approaches.

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Section: Literature Reviewmentioning

confidence: 99%

Optimal Deep Learning Based Intruder Identification in Industrial Internet of Things Environment

Alalayah¹,

Alrayes²,

Alaidarous³

et al. 2023

Computer Systems Science and Engineering

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“…Moreover, it is known that NIDS designed for IoT systems use case scenarios based on MEC require high quality of service, reduced latency, high throughput, and real-time operation. Researchers desist from the use of cloud computing due to its crucial drawback of low propagation delay (high latency) [ 101 , 102 ]. The most influential features that make IoT system developers select MEC over cloud computing for NIDS design include: Providing security context-awareness (the capability of the MEC server to disseminate real-time security information to the IoT device) [ 103 ]; Energy-saving during and after data transfer; Improvement of privacy/security in IoT (users are deprived of the total ownership of their data in cloud computing, which results in private data leakage and loss) [ 104 ]; Optimal resource allocation by the MEC for the IoT system [ 105 ].…”

Section: Multi-access Edge Computing As a Resource To Provide Securit...mentioning

confidence: 99%

Intrusion Detection in Internet of Things Systems: A Review on Design Approaches Leveraging Multi-Access Edge Computing, Machine Learning, and Datasets

Gyamfi

Jurcut

2022

Sensors

Self Cite

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The explosive growth of the Internet of Things (IoT) applications has imposed a dramatic increase of network data and placed a high computation complexity across various connected devices. The IoT devices capture valuable information, which allows the industries or individual users to make critical live dependent decisions. Most of these IoT devices have resource constraints such as low CPU, limited memory, and low energy storage. Hence, these devices are vulnerable to cyber-attacks due to the lack of capacity to run existing general-purpose security software. It creates an inherent risk in IoT networks. The multi-access edge computing (MEC) platform has emerged to mitigate these constraints by relocating complex computing tasks from the IoT devices to the edge. Most of the existing related works are focusing on finding the optimized security solutions to protect the IoT devices. We believe distributed solutions leveraging MEC should draw more attention. This paper presents a comprehensive review of state-of-the-art network intrusion detection systems (NIDS) and security practices for IoT networks. We have analyzed the approaches based on MEC platforms and utilizing machine learning (ML) techniques. The paper also performs a comparative analysis on the public available datasets, evaluation metrics, and deployment strategies employed in the NIDS design. Finally, we propose an NIDS framework for IoT networks leveraging MEC.

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“…In comparison to existing IDS, our approach surpasses them with more over 99.9% in terms of ACC, recall, accuracy, and F1-score metrics with record detection and calculation speed. Gyam et al [41] present a thorough examination of cutting-edge network intrusion detection systems (NIDS) and IoT network security practices in 2022. They looked at systems built on MEC platforms and using ML approaches.…”

Section: Related Workmentioning

confidence: 99%

DEIGASe: Deep Extraction and Information Gain for an Optimal Anomaly Detection in IoT-based Smart Cities

Hazman

Guezzaz

Benkirane

et al. 2022

Preprint

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A smart city architecture involves the integration of information and communication technology with gadgets across a system in order to boost connectivity for residents. As a result of ongoing data collection to improve service to customers. With the availability of multiple devices and remote flow through channels, the probability of cyber-attacks and intrusion detection has increased. As a consequence, numerous solutions for securing IoT have been implemented, including authentication, availability, encryption, and data integrity. Intrusion detection systems (IDSs) are an effective cyber solution that could be expanded by utilizing machine learning (ML) and deep learning (DP) techniques. This study presents an enhanced IDS that makes use of This study provides an optimal anomaly detection model, called DEIGASe which combines deep extraction based on the stacked autoencoder and feature selection utilizing Information gain (IG) and Genetic algorithms (GA) for select best features. The proposed model was evaluated on the upgraded IoT-23, BoT-IoT, and Edge-IIoT datasets using the GPU. When compared to existing IDS, our approach provides good ACC, recall, and precision rating performance features, with over 99.9% on record detection and calculation times around 17s for learning and 0.613s for detection.

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Novel Online Network Intrusion Detection System for Industrial IoT Based on OI-SVDD and AS-ELM

Cited by 37 publications

References 36 publications

Optimal Deep Learning Based Intruder Identification in Industrial Internet of Things Environment

Optimal Deep Learning Based Intruder Identification in Industrial Internet of Things Environment

Intrusion Detection in Internet of Things Systems: A Review on Design Approaches Leveraging Multi-Access Edge Computing, Machine Learning, and Datasets

DEIGASe: Deep Extraction and Information Gain for an Optimal Anomaly Detection in IoT-based Smart Cities

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