Machine Learning-Driven Security System for Next-Generation Ultra-Low-Latency Financial Network Governance

This paper suggests applying to the construction of an adaptive security system in ultra-low-latency financial networks the use of the Reinforcement Learning (RL). With the current fast changing financial environment, it is important to have real time security governance to prevent any cyber threats and still manage the speed of transactions. The suggested system takes advantage of the state-of-the-art RL algorithm, Proximal Policy Optimization (PPO), to learn optimal security policies dynamically given a real-time feedback of the network. PPO modifies security measures automatically and reduces threats like fraud or unauthorized access without affecting the low-latency requirements of financial systems. The system is self adapting to the new threats which it constantly interacts with, and improves its defence mechanisms as time goes by. The results of the simulation have shown that the method can enhance the resilience of security in high-frequency trading systems and show a significant decrease in response time and an increase in detection efficiency. The RL into financial governance forms a new template of self-optimizing proactive security systems in the next generation financial networks.