A Unified Neural-Symbolic Intelligence Model for Real-Time Threat Detection, Incident Response Orchestration, and Critical Infrastructure Defense

• Abbas Ahsun

  Texas University

  Author

Critical infrastructure sectors face increasingly sophisticated cyber threats that bypass conventional signature-based defenses, yet existing AI-driven security solutions remain fragmented across detection, response, and orchestration functions. This study addresses the critical gap in unified architectures capable of simultaneously delivering real-time threat detection, automated incident response, and explainable decision-making for operational technology environments. We propose a neural-symbolic intelligence model that integrates graph neural networks, transformer-based temporal reasoning, and ontology-driven symbolic inference within a modular agentic architecture. The framework was validated using a retrospective analysis of the CICIDS2017, UNSW-NB15, and CICIoT2023 benchmark datasets, supplemented by simulated multi-stage attack scenarios representative of critical infrastructure threats. The unified model achieved 89.4% detection accuracy for coordinated multi-vector attacks, outperforming baseline deep learning models by 12.7 percentage points while reducing mean time to response by 43% compared to rule-based security orchestration systems. Feature importance analysis identified temporal dependency patterns and cross-domain correlation signals as the most influential predictors. The study contributes a replicable architecture for integrating symbolic reasoning with neural perception in security operations, offering practitioners a pathway toward transparent, autonomous, and context-aware critical infrastructure defense. Implications extend to standards development, SOC workflow redesign, and future research on adversarial robustness in hybrid AI systems.

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Copyright (c) 2026 Abbas Ahsun (Author)

This work is licensed under a Creative Commons Attribution 4.0 International License.