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<p>The convergence of cyber-physical systems, the Industrial Internet of Things (IIoT), and edge computing in Industry 4.0 has dramatically expanded the attack surfaces of industrial networks, making traditional intrusion detection systems (IDS) increasingly inadequate. While artificial intelligence (AI) and machine learning (ML) offer promising solutions, existing surveys often lack a specific focus on Industry 4.0 and a critical evaluation of the deployment feasibility. This systematic literature review (SLR) addresses these gaps through a PRISMA-guided analysis of AI-driven IDS research published between 2020 and 2025. From more than 8,000 studies, 22 high-quality papers were selected for detailed evaluation, revealing a pronounced shift towards edge-enabled detection architectures, hybrid AI models balancing accuracy and interpretability, and the integration of explainable AI (XAI) to strengthen operator trust. Key challenges persist, including reliance on synthetic datasets, limited validation in operational environments, computational demands unsuitable for resource-constrained edge devices, and integration issues with legacy operational technology (OT). The review’s contributions include a unified taxonomy mapping AI techniques to Industry 4.0 threats, a comparative analysis highlighting emerging trends such as federated learning and digital twins, and a research roadmap that emphasises lightweight models, realistic industrial datasets, and proactive autonomous response mechanisms. This SLR bridges the gap between academic innovation and practical deployment, supporting secure, intelligent manufacturing ecosystems.</p>

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Securing industry 4.0: a systematic review of AI-driven intrusion detection approaches and emerging trends

  • 期刊论文

  • 发表日期：

    2025年12月22日

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  • Nethma Kalpani,
  • Nureka Rodrigo

摘要

The convergence of cyber-physical systems, the Industrial Internet of Things (IIoT), and edge computing in Industry 4.0 has dramatically expanded the attack surfaces of industrial networks, making traditional intrusion detection systems (IDS) increasingly inadequate. While artificial intelligence (AI) and machine learning (ML) offer promising solutions, existing surveys often lack a specific focus on Industry 4.0 and a critical evaluation of the deployment feasibility. This systematic literature review (SLR) addresses these gaps through a PRISMA-guided analysis of AI-driven IDS research published between 2020 and 2025. From more than 8,000 studies, 22 high-quality papers were selected for detailed evaluation, revealing a pronounced shift towards edge-enabled detection architectures, hybrid AI models balancing accuracy and interpretability, and the integration of explainable AI (XAI) to strengthen operator trust. Key challenges persist, including reliance on synthetic datasets, limited validation in operational environments, computational demands unsuitable for resource-constrained edge devices, and integration issues with legacy operational technology (OT). The review’s contributions include a unified taxonomy mapping AI techniques to Industry 4.0 threats, a comparative analysis highlighting emerging trends such as federated learning and digital twins, and a research roadmap that emphasises lightweight models, realistic industrial datasets, and proactive autonomous response mechanisms. This SLR bridges the gap between academic innovation and practical deployment, supporting secure, intelligent manufacturing ecosystems.