The Convergence of Blockchain and Deep Learning in Securing Cyber-Physical Systems and FinTech Infrastructures: A Multidimensional Analysis of Threats, Mitigation, and Future Paradigms

Abstract

The rapid digitalization of critical infrastructures and financial services has necessitated a paradigm shift in cybersecurity strategies. Cyber-Physical Systems (CPS), the Internet of Medical Things (IoMT), and Financial Technology (FinTech) platforms are increasingly susceptible to complex vulnerabilities that transcend traditional security boundaries. This research provides an exhaustive investigation into the integration of Blockchain and Deep Learning-specifically Transformer-CNN frameworks and Auto-Encoders-to mitigate emerging threats such as data injection, man-in-the-middle attacks, and sophisticated fraud. By synthesizing current literature on Industrial Control Systems (ICS) and SCADA vulnerabilities, the paper establishes a theoretical foundation for decentralized, cognitively autonomous security architectures. The study explores the efficacy of multilevel intrusion detection, trusted token authentication, and post-quantum blockchain considerations. Findings indicate that while Deep Learning offers superior anomaly detection in high-dimensional time-series data, Blockchain provides the immutable ledger necessary for decentralized trust. The convergence of these technologies facilitates a self-healing security ecosystem capable of addressing the challenges of multi-cloud environments and real-time digital payment fraud. The paper concludes with a critical assessment of the trade-offs between system performance and cryptographic robustness in the era of software-defined networks.