Enhancing Cybersecurity Protocols in Financial Networks through Reinforcement Learning

Authors

  • Comfort Idongesit Michael Department of Computer and Information Sciences, Northumbria University London, United Kingdom
  • Trudy-Ann Campbell School of Engineering Prairie View, A and M University Prairie View, Texas. USA.
  • Idoko Peter Idoko Department of Electrical/ Electronic Engineering, University of Ibadan, Nigeria
  • Ogoniba Unity Bemologi College of Law, University of Derby, United Kingdom
  • Abraham Peter Anyebe Department of Navigation and Direction, Nigerian Navy Naval Unit, Abuja, Nigeria
  • Idoko Innocent Odeh Professional Services Department Layer3 Ltd, Wuse Zone 4, Abuja, Nigeria

DOI:

https://doi.org/10.38124/ijsrmt.v3i9.58

Keywords:

Cybersecurity, Protocols, Financial Networks, Reinforcement Learning, Quantum Computing, Data Science Integration

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.

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Published

2024-10-09

How to Cite

Michael, C. I., Campbell, T.-A., Idoko, I. P., Bemologi, O. U., Anyebe, A. P., & Odeh, I. I. (2024). Enhancing Cybersecurity Protocols in Financial Networks through Reinforcement Learning. International Journal of Scientific Research and Modern Technology, 3(9). https://doi.org/10.38124/ijsrmt.v3i9.58

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