Blockchain-Integrated Deep Learning Framework for Cyber-Physical Anomaly Detection and Secure Transaction Intelligence in Smart IoT Infrastructures
Keywords:
Blockchain security, cyber-physical systems, anomaly detection, deep learningAbstract
The rapid expansion of cyber-physical systems, Internet of Things infrastructures, intelligent smart grids, and blockchain-enabled digital ecosystems has fundamentally transformed modern industrial and urban environments. However, the increasing interconnectivity among devices, cloud infrastructures, communication networks, and automated control systems has also amplified vulnerabilities associated with false data injection attacks, transaction fraud, anomaly propagation, energy manipulation, and distributed cyber intrusions. Traditional security mechanisms are increasingly unable to cope with the dynamic and decentralized characteristics of contemporary smart environments. This study develops a comprehensive research framework integrating blockchain technology, deep learning architectures, reinforcement learning strategies, and intelligent anomaly detection mechanisms for secure cyber-physical operations in smart infrastructures. The research synthesizes theoretical and empirical findings from recent literature involving blockchain-enabled transaction systems, deep neural anomaly detection models, recurrent neural networks, autoencoders, transformer architectures, and machine learning-based predictive frameworks. Particular emphasis is placed on the convergence between secure distributed ledger technologies and intelligent predictive analytics for real-time threat mitigation. The study explores how blockchain enhances transparency, immutability, decentralized trust, and secure transaction validation while deep learning contributes adaptive detection capabilities for evolving attack patterns. Furthermore, the article examines the application of BiLSTM attention models, reinforcement learning occupancy detection, cloud workload prediction, wireless sensor optimization, and transformer convolutional architectures in improving cyber resilience. Results indicate that integrated blockchain-artificial intelligence ecosystems substantially improve detection accuracy, operational reliability, predictive intelligence, and system scalability compared to conventional centralized security frameworks. The discussion highlights theoretical implications concerning decentralized intelligence, adaptive security automation, and cyber-physical trust formation. Limitations related to computational overhead, interoperability complexity, energy consumption, and ethical governance are critically analyzed. The article concludes that future smart infrastructures will increasingly depend on synergistic blockchain and deep learning ecosystems capable of autonomous threat recognition, resilient transaction validation, and scalable cyber-physical protection.
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