Blockchain-Integrated Artificial Intelligence Frameworks for Cybersecurity, Anomaly Detection, and Resilient Cyber-Physical Infrastructure in Smart Financial and Industrial Ecosystems
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Department of Computer Science and Digital Systems, University of Edinburgh, United Kingdom
Abstract
The rapid evolution of cyber-physical systems, decentralized digital infrastructures, intelligent industrial networks, and smart financial ecosystems has significantly transformed the cybersecurity landscape of modern organizations. Simultaneously, the expansion of artificial intelligence, blockchain technology, edge computing, cloud infrastructures, and Internet of Things environments has intensified concerns regarding cyberattacks, anomaly detection, identity compromise, fraud, ransomware, phishing, and operational disruption. Traditional cybersecurity architectures increasingly struggle to manage highly dynamic and intelligent attack environments characterized by decentralized connectivity, autonomous devices, and real-time digital interactions. Consequently, researchers and institutions have focused on integrating blockchain and artificial intelligence technologies to establish adaptive, decentralized, explainable, and resilient cybersecurity frameworks capable of supporting next-generation digital infrastructures.
This study critically investigates the convergence of blockchain technology and artificial intelligence in cybersecurity applications across smart financial systems, cyber-physical environments, industrial control systems, edge computing ecosystems, smart cities, and anomaly detection architectures. The research adopts a qualitative interpretive methodology based on extensive theoretical synthesis of contemporary academic literature related to blockchain-enabled cybersecurity, AI-driven anomaly detection, cyber governance, fraud prevention, federated learning, explainable artificial intelligence, and intelligent cyber-defense systems.
The findings reveal that artificial intelligence significantly enhances predictive threat analysis, autonomous anomaly detection, adaptive intrusion monitoring, and intelligent cyber-risk management, while blockchain contributes decentralization, immutability, transparency, trust management, and secure distributed authentication. Their integration creates synergistic cybersecurity ecosystems capable of improving digital trust, cyber resilience, operational continuity, and secure information exchange within complex interconnected infrastructures. The study further identifies substantial implementation challenges involving scalability, governance complexity, computational overhead, explainability, interoperability, ethical concerns, and regulatory uncertainty.
The research concludes that blockchain-integrated artificial intelligence architectures represent a transformative direction for future cybersecurity systems. Their application across industrial control systems, smart banking, IoT environments, and decentralized digital ecosystems may redefine cybersecurity governance and digital trust management in increasingly interconnected global infrastructures.
How to Cite
Eleanor V. Hartmann. (2026). Blockchain-Integrated Artificial Intelligence Frameworks for Cybersecurity, Anomaly Detection, and Resilient Cyber-Physical Infrastructure in Smart Financial and Industrial Ecosystems. Frontiers in Emerging Multidisciplinary Sciences, 3(02), 75–85. Retrieved from https://irjernet.com/index.php/fems/article/view/389
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