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<p>Industry 5.0’s increasing integration of IT and OT systems is transforming industrial operations but also expanding the cyber–physical attack surface. Industrial Control Systems (ICS) face escalating security challenges as traditional siloed defenses fail to provide coherent, cross-domain threat insights. We present BRIDG-ICS (BRIDge for Industrial Control Systems), an AI-enriched Knowledge Graph (KG) framework for context-aware threat analysis and quantitative assessment of cyber resilience in smart manufacturing environments. BRIDG-ICS fuses heterogeneous industrial and cybersecurity data into an integrated Industrial Security Knowledge Graph linking assets, vulnerabilities, and adversarial behaviors with probabilistic risk metrics (e.g., exploit likelihood, attack cost). This unified graph representation enables multi-stage attack path simulation using graph-analytic techniques. To enrich the graph’s semantic depth, the framework leverages domain-specific pretrained language models (e.g., SecureBERT, CySecBERT) to extract cybersecurity entities, infer relationships, and transform natural-language threat descriptions into structured graph triples, thereby populating the knowledge graph with missing associations and latent risk indicators. The resulting AI-enriched KG supports multi-hop threat reasoning through graph-based inference, improving visibility into complex attack chains and guiding data-driven mitigation. In simulated industrial scenarios, BRIDG-ICS scales well, reduces potential attack exposure, and can enhance cyber–physical system resilience in Industry 5.0 settings.</p>

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Bridg-ics: AI-grounded knowledge graphs for intelligent threat analytics in industry 5.0 cyber-physical systems

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月14日

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  • Padmeswari Nandiya,
  • Ahmad Mohsin,
  • Ahmed Ibrahim,
  • Iqbal H. Sarker,
  • Helge Janicke

摘要

Industry 5.0’s increasing integration of IT and OT systems is transforming industrial operations but also expanding the cyber–physical attack surface. Industrial Control Systems (ICS) face escalating security challenges as traditional siloed defenses fail to provide coherent, cross-domain threat insights. We present BRIDG-ICS (BRIDge for Industrial Control Systems), an AI-enriched Knowledge Graph (KG) framework for context-aware threat analysis and quantitative assessment of cyber resilience in smart manufacturing environments. BRIDG-ICS fuses heterogeneous industrial and cybersecurity data into an integrated Industrial Security Knowledge Graph linking assets, vulnerabilities, and adversarial behaviors with probabilistic risk metrics (e.g., exploit likelihood, attack cost). This unified graph representation enables multi-stage attack path simulation using graph-analytic techniques. To enrich the graph’s semantic depth, the framework leverages domain-specific pretrained language models (e.g., SecureBERT, CySecBERT) to extract cybersecurity entities, infer relationships, and transform natural-language threat descriptions into structured graph triples, thereby populating the knowledge graph with missing associations and latent risk indicators. The resulting AI-enriched KG supports multi-hop threat reasoning through graph-based inference, improving visibility into complex attack chains and guiding data-driven mitigation. In simulated industrial scenarios, BRIDG-ICS scales well, reduces potential attack exposure, and can enhance cyber–physical system resilience in Industry 5.0 settings.