Enhancing Cybersecurity Protocols in Financial Networks through Reinforcement Learning

Abstract

Cybersecurity in financial networks is facing an unprecedented level of sophistication from cyber threats, necessitating the adoption of advanced technologies to safeguard sensitive financial data. This review paper explores the integration of Reinforcement Learning (RL), Quantum Computing (QC), and Data Science (DS) to enhance cybersecurity protocols in financial networks. RL offers promising solutions for automating threat detection, intrusion prevention, and response systems by leveraging adaptive learning techniques. QC introduces powerful computational capabilities to both strengthen encryption methods and challenge traditional cryptographic systems, while DS provides data-driven insights for predictive analytics and real-time anomaly detection. By examining the application of these technologies individually and in tandem, this paper highlights their potential to transform financial cybersecurity. We discuss existing case studies and research developments, focusing on their contributions to threat intelligence, encryption, and network defense. The paper also identifies the key challenges associated with implementing RL, QC, and DS, including scalability, hardware limitations, and integration complexities. In conclusion, we provide insights into future research directions aimed at addressing these challenges, presenting a roadmap for fully integrating RL, QC, and DS into financial cybersecurity frameworks. This comprehensive review underscores the critical role these technologies will play in safeguarding financial systems against emerging cyber threats.