A Novel Attack Detection Scheme for the Industrial Internet of Things Using a Lightweight Random Neural Network

Latif, Shahid; Zou, Zhuo; Idrees, Zeba; Ahmad, Jawad

doi:10.1109/access.2020.2994079

Cited by 143 publications

(74 citation statements)

References 40 publications

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“…Third, the feasibility of the proposed schemes for resource-constrained devices is not deeply considered. To address the aforementioned challenges, we propose a lightweight HDRaNN-based attack detection scheme for IIoT networks, which is an extension of our previous work [23]. We use two latest IIoT securityrelated datasets and define several performance parameters for the performance evaluation.…”

Section: Related Workmentioning

confidence: 99%

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A Hybrid Deep Random Neural Network for Cyberattack Detection in the Industrial Internet of Things

Huma¹,

Latif

Ahmad

et al. 2021

IEEE Access

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The Industrial Internet of Things (IIoT) refers to the use of traditional Internet of Things (IoT) concepts in industrial sectors and applications. IIoT has several applications in smart homes, smart cities, smart grids, connected cars, and supply chain management. However, these systems are being more frequently targeted by cybercriminals. Deep learning and big data analytics have great potential in designing and developing robust security mechanisms for IIoT networks. In this paper, a novel hybrid deep random neural network (HDRaNN) for cyberattack detection in the IIoT is presented. The HDRaNN combines a deep random neural network and a multilayer perceptron with dropout regularization. The proposed technique is evaluated using two IIoT security-related datasets: (i) DS2OS and (ii) UNSW-NB15. The performance of the proposed scheme is analyzed through a number of performance metrics such as accuracy, precision, recall, F1 score, log loss, Region of Convergence (ROC), and Area Under the Curve (AUC). The HDRaNN classified 16 different types of cyberattacks using with higher accuracy of 98% and 99% for DS2OS and UNSW-NB15, respectively. To measure the effectiveness of the proposed scheme, the performance metrics are also compared with several state-of-the-art attack detection algorithms. The findings of HDRaNN proved its superior performance over other DL-based schemes. The deployment perspective of the proposed work is also highlighted in this work.

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Section: Related Workmentioning

confidence: 99%

“…It contains 13 features and seven different types of attacks such as denial of service, malicious operation, malicious control, wrong setup, spying, scan, and data type probing attacks. All the classes of this dataset are shortly described in the following [23].…”

Section: ) Ds2os Datasetmentioning

confidence: 99%

A Hybrid Deep Random Neural Network for Cyberattack Detection in the Industrial Internet of Things

Huma¹,

Latif

Ahmad

et al. 2021

IEEE Access

Self Cite

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“…Finally, it is noteworthy to mention that, since the proposed AIDS is designed to be deployed in resource-constrained devices, deep learning techniques, which are complex, heavyweight, and are characterized by a high computational overhead, have not been considered. However, deep learning techniques, such as those mentioned in [39,40], could be considered, as future work, in the extension of the autonomous and lightweight proposed AIDS to a cloud-based AIDS for IoMT edge networks.…”

Section: Challenges and Future Workmentioning

confidence: 99%

An Anomaly-Based Intrusion Detection System for Internet of Medical Things Networks

et al. 2021

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Over the past few years, the healthcare sector is being transformed due to the rise of the Internet of Things (IoT) and the introduction of the Internet of Medical Things (IoMT) technology, whose purpose is the improvement of the patient’s quality of life. Nevertheless, the heterogenous and resource-constrained characteristics of IoMT networks make them vulnerable to a wide range of threats. Thus, novel security mechanisms, such as accurate and efficient anomaly-based intrusion detection systems (AIDSs), considering the inherent limitations of the IoMT networks, need to be developed before IoMT networks reach their full potential in the market. Towards this direction, in this paper, we propose an efficient and effective anomaly-based intrusion detection system (AIDS) for IoMT networks. The proposed AIDS aims to leverage host-based and network-based techniques to reliably collect log files from the IoMT devices and the gateway, as well as traffic from the IoMT edge network, while taking into consideration the computational cost. The proposed AIDS is to rely on machine learning (ML) techniques, considering the computation overhead, in order to detect abnormalities in the collected data and thus identify malicious incidents in the IoMT network. A set of six popular ML algorithms was tested and evaluated for anomaly detection in the proposed AIDS, and the evaluation results showed which of them are the most suitable.

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“…The proposed model learned effectively in the training phase. Shahid et al [69] proposed a new IDS based on a random neural network (RaNN) approach. The proposed prediction based on the RaNN achieved a higher performance than other machine learning algorithms such as ANN, SVM, and DT.…”

Section: Machine Learning Techniquesmentioning

confidence: 99%

Monitoring Real Time Security Attacks for IoT Systems Using DevSecOps: A Systematic Literature Review

et al. 2021

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In many enterprises and the private sector, the Internet of Things (IoT) has spread globally. The growing number of different devices connected to the IoT and their various protocols have contributed to the increasing number of attacks, such as denial-of-service (DoS) and remote-to-local (R2L) ones. There are several approaches and techniques that can be used to construct attack detection models, such as machine learning, data mining, and statistical analysis. Nowadays, this technique is commonly used because it can provide precise analysis and results. Therefore, we decided to study the previous literature on the detection of IoT attacks and machine learning in order to understand the process of creating detection models. We also evaluated various datasets used for the models, IoT attack types, independent variables used for the models, evaluation metrics for assessment of models, and monitoring infrastructure using DevSecOps pipelines. We found 49 primary studies, and the detection models were developed using seven different types of machine learning techniques. Most primary studies used IoT device testbed datasets, and others used public datasets such as NSL-KDD and UNSW-NB15. When it comes to measuring the efficiency of models, both numerical and graphical measures are commonly used. Most IoT attacks occur at the network layer according to the literature. If the detection models applied DevSecOps pipelines in development processes for IoT devices, they were more secure. From the results of this paper, we found that machine learning techniques can detect IoT attacks, but there are a few issues in the design of detection models. We also recommend the continued use of hybrid frameworks for the improved detection of IoT attacks, advanced monitoring infrastructure configurations using methods based on software pipelines, and the use of machine learning techniques for advanced supervision and monitoring.

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A Novel Attack Detection Scheme for the Industrial Internet of Things Using a Lightweight Random Neural Network

Cited by 143 publications

References 40 publications

A Hybrid Deep Random Neural Network for Cyberattack Detection in the Industrial Internet of Things

A Hybrid Deep Random Neural Network for Cyberattack Detection in the Industrial Internet of Things

An Anomaly-Based Intrusion Detection System for Internet of Medical Things Networks

Monitoring Real Time Security Attacks for IoT Systems Using DevSecOps: A Systematic Literature Review

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