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

Huma, Zil e; Latif, Shahid; Ahmad, Jawad; Idrees, Zeba; Ibrar, Anas; Zou, Zhuo; Alqahtani, Fehaid; Baothman, Fatmah

doi:10.1109/access.2021.3071766

Cited by 67 publications

(69 citation statements)

References 24 publications

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“…Firms showed benefits, such as an increase of productivity, and faster time responses to suppliers and consumers. RA from the Adoption of decision-making processes is more relevant for organizations in logistics, maritime (Mileski et al, 2018) and manufacturing organizations (Huma et al, 2021;Khaw et al, 2021) than for those organizations in the service industry. On a comparative note, Yeboah-Ofori et al (2021), highlighted the following sectors as cyber vulnerable: Energy, Communication, Transport, Health-care and Manufacturing, and calls on cybersecurity to include the global delivery of physical goods plus the delivery of software.…”

Section: Discussionmentioning

confidence: 99%

Eleven years of cyberattacks on Chinese supply chains in an era of cyber warfare, a review and future research agenda

Pérez-Morón

2021

JABS

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Purpose The contribution of this study aims to twofold: First, it provides an overview of the current state of research on cyberattacks on Chinese supply chains (SCs). Second, it offers a look at the Chinese Government’s approach to fighting cyberattacks on Chinese SCs and its calls for global governance. Design/methodology/approach A comprehensive literature review was conducted on Clarivate Analytics’ Web of Science, in Social Sciences Citation Index journals, Scopus and Google Scholar, published between 2010–2021. A systematic review of practitioner literature was also conducted. Findings Chinese SCs have become a matter of national security, especially in the era of cyber warfare. The risks to SC have been outlined. Cybersecurity regulations are increasing as China aims to build a robust environment for cyberspace development. Using the Technology-organization-environment (TOE) framework, the results show that the top five factors influencing the adoption process in firms are as follows: relative advantage and technological readiness (Technology context); top management support and firm size (Organization context) and government policy and regulations (Environment context). Research limitations/implications This review focuses on cyberattacks on Chinese SCs and great care was taken when selecting search terms. However, the author acknowledges that the choice of databases/terms may have excluded a few articles on cyberattacks from this review. Practical implications This review provides managerial insights for SC practitioners into how cyberattacks have the potential to disrupt the global SC network. Originality/value Past researchers proposed a taxonomic approach to evaluate progress with SC integration into Industry 4.0; in contrast, this study is one of the first steps toward an enhanced understanding of cyberattacks on Chinese SCs and their contribution to the global SC network using the TOE framework.

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Section: Discussionmentioning

confidence: 99%

Eleven years of cyberattacks on Chinese supply chains in an era of cyber warfare, a review and future research agenda

Pérez-Morón

2021

JABS

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“…Refs. [21,22], authors found that the conventional Internet of Things (IoT) technology is used in the industrial sector. The extensive expansion of IoT in the industrial sector gives rise to the obstruction of information security.…”

Section: Related Workmentioning

confidence: 99%

Optimization of Intrusion Detection Systems Determined by Ameliorated HNADAM-SGD Algorithm

et al. 2022

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Information security is of pivotal concern for consistently streaming information over the widespread internetwork. The bottleneck flow of incoming and outgoing data traffic introduces the issues of malicious activities taken place by intruders, hackers and attackers in the form of authenticity obstruction, gridlocking data traffic, vandalizing data and crashing the established network. The issue of emerging suspicious activities is managed by the domain of Intrusion Detection Systems (IDS). The IDS consistently monitors the network for the identification of suspicious activities, and generates alarm and indication in the presence of malicious threats and worms. The performance of IDS is improved by using different machine learning algorithms. In this paper, the Nesterov-Accelerated Adaptive Moment Estimation–Stochastic Gradient Descent (HNADAM-SDG) algorithm is proposed to determine the performance of Intrusion Detection Systems IDS. The algorithm is used to optimize IDS systems by hybridization and tuning of hyperparameters. The performance of algorithm is compared with other classification algorithms such as logistic regression, ridge classifier and ensemble algorithms where the experimental analysis and computations show the improved accuracy with 99.8%, sensitivity with 99.7%, and specificity with 99.5%.

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“…Smart industrial frameworks require multiple synchronized processes that require lower latency and higher reliability to obtain the desired performance [164,165]. Furthermore, the DL techniques used in IIoT should be capable of dealing with these challenges as well as other aspects such as resource management and network deployment [166].…”

Section: Low Latency and Improved Reliabilitymentioning

confidence: 99%

Deep Learning for the Industrial Internet of Things (IIoT): A Comprehensive Survey of Techniques, Implementation Frameworks, Potential Applications, and Future Directions

Latif

Driss

Boulila

et al. 2021

Sensors

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The Industrial Internet of Things (IIoT) refers to the use of smart sensors, actuators, fast communication protocols, and efficient cybersecurity mechanisms to improve industrial processes and applications. In large industrial networks, smart devices generate large amounts of data, and thus IIoT frameworks require intelligent, robust techniques for big data analysis. Artificial intelligence (AI) and deep learning (DL) techniques produce promising results in IIoT networks due to their intelligent learning and processing capabilities. This survey article assesses the potential of DL in IIoT applications and presents a brief architecture of IIoT with key enabling technologies. Several well-known DL algorithms are then discussed along with their theoretical backgrounds and several software and hardware frameworks for DL implementations. Potential deployments of DL techniques in IIoT applications are briefly discussed. Finally, this survey highlights significant challenges and future directions for future research endeavors.

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

Cited by 67 publications

References 24 publications

Eleven years of cyberattacks on Chinese supply chains in an era of cyber warfare, a review and future research agenda

Eleven years of cyberattacks on Chinese supply chains in an era of cyber warfare, a review and future research agenda

Optimization of Intrusion Detection Systems Determined by Ameliorated HNADAM-SGD Algorithm

Deep Learning for the Industrial Internet of Things (IIoT): A Comprehensive Survey of Techniques, Implementation Frameworks, Potential Applications, and Future Directions

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