AI-Driven Predictive Cyber Threat Intelligence for Critical Infrastructure Protection
DOI:
https://doi.org/10.63282/3050-9262.IJAIDSML-V7I3P106Keywords:
Artificial Intelligence, Cyber Threat Intelligence, Critical Infrastructure Protection, Machine Learning, Predictive Analytics, Risk Scoring, Intrusion Detection, MITRE ATT&CK, NIST Cybersecurity FrameworkAbstract
Critical infrastructure systems such as energy, telecommunications, transportation, healthcare, water, and government services increasingly depend on interconnected information technology, operational technology, cloud, and Internet of Things environments. This growing interconnectivity expands the attack surface and increases the consequences of cyber incidents. Traditional defensive approaches remain essential, but many are reactive and depend on known indicators, static rules, or post-event investigation. This paper proposes an AI-driven Predictive Cyber Threat Intelligence Framework for critical infrastructure protection. The framework integrates multi-source cyber threat intelligence, network and endpoint telemetry, vulnerability information, adversary behavior models, machine learning, anomaly detection, risk scoring, explainable analytics, and response orchestration. The proposed model is designed to support proactive threat anticipation, prioritization of high-risk assets, reduction of alert fatigue, and faster response to emerging attacks. The paper also presents a literature-based comparison with existing approaches, a methodology for future experimental validation using public intrusion detection datasets, and implementation considerations for critical infrastructure operators. The contribution of this work is a practical, modular, and standards-aligned framework that connects cyber threat information sharing, AI-based prediction, criticality-aware risk scoring, and operational decision support for high-impact environments.
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