BRL-ETDM: Bayesian reinforcement learning-based explainable threat detection model for industry 5.0 network

Dey, Arun Kumar; Gupta, Govind P.; Sahu, Satya Prakash

doi:10.1007/s10586-024-04422-6

Cluster Comput

2024

DOI: 10.1007/s10586-024-04422-6

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BRL-ETDM: Bayesian reinforcement learning-based explainable threat detection model for industry 5.0 network

Arun Kumar Dey,

Govind P. Gupta,

Satya Prakash Sahu

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Cited by 2 publications

References 45 publications

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EMTD: Explainable Malicious Traffic Detection Model Using Hybrid Deep Learning Techniques for Industrial IoT Networks

Dey,

Gupta,

Sahu

2024

Arab J Sci Eng

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EMTD: Explainable Malicious Traffic Detection Model Using Hybrid Deep Learning Techniques for Industrial IoT Networks

Dey,

Gupta,

Sahu

2024

Arab J Sci Eng

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Cybersecurity for Industry 5.0: trends and gaps

Kour,

Karim,

Dersin

et al. 2024

Front. Comput. Sci.

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Industry 5.0 promises to revolutionize the industry by focusing on human-centric, sustainability, and resilience empowered by emerging technologies such as Artificial Intelligence (AI) and digitalization. This paradigm shift is expected to bring significant advancements in sustainability, resilience, productivity, effectiveness, efficiency, customization, reliability, safety, security, maintainability etc. However, this shift of the industrial paradigm introduces substantial cybersecurity challenges due to the increased attack surface and data sensitivity. Therefore, the objective of this paper is to conduct a thorough literature review of the recent research on cybersecurity in Industry 5.0, highlighting emerging trends, gaps, and potential solutions. To conduct this research, the authors have applied the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology to investigate cybersecurity solutions in Industry 5.0. The findings reveal that conceptual research dominates, with AI, Blockchain, and Internet of Things (IoT) most prevalent but highlights a gap in linking cybersecurity to resilience and sustainability. Furthermore, the paper aims to present trends in cybersecurity research with more relevant results from 2022 to 2024. It conducts a thorough review of the literature, highlighting the evolving landscape of cybersecurity applications in Industry 5.0.

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BRL-ETDM: Bayesian reinforcement learning-based explainable threat detection model for industry 5.0 network

Cited by 2 publications

References 45 publications

EMTD: Explainable Malicious Traffic Detection Model Using Hybrid Deep Learning Techniques for Industrial IoT Networks

EMTD: Explainable Malicious Traffic Detection Model Using Hybrid Deep Learning Techniques for Industrial IoT Networks

Cybersecurity for Industry 5.0: trends and gaps

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