## 5 Conclusions and Future Work  
This paper presents ARCS, a novel reinforcement learning framework for automated cybersecurity incident response that effectively addresses the challenges of real-time threat mitigation and adaptive defense strategy optimization. Through extensive experimentation on a comprehensive dataset of 20,000 security incidents, we demonstrate that ARCS achieves significant improvements over existing approaches, with a 89.2% response effectiveness rate and 23.5 s average response time. The dual-stream architecture, combining immediate response optimization with strategic planning, proves particularly effective in handling complex multi-stage attacks while maintaining system stability. The framework's success in reducing false positive rates to 3.9% and its ability to scale efficiently with increasing event volumes make it particularly valuable for practical deployment scenarios. The experimental results demonstrate robust performance across different attack types, from conventional DDoS attacks to sophisticated APT campaigns, indicating the framework's versatility in real-world applications. The case studies further validate ARCS's effectiveness in protecting critical assets and maintaining system integrity under diverse attack scenarios. However, our current approach has several limitations that warrant further investigation. First, while ARCS shows strong performance in handling known attack patterns, its ability to generalize to completely novel attack vectors could be further improved. The current state representation mechanism, although effective, may not capture all relevant aspects of previously unseen attack patterns. Additionally, the framework's reliance on historical attack data for initial training might limit its effectiveness against zero-day exploits that significantly deviate from known patterns. The computational overhead of maintaining dual policy networks, while manageable in our experiments, could become significant in extremely large-scale deployments. Furthermore, the current implementation assumes the availability of clean, well-structuredsecurity event data, which may not always be the case in real-world scenarios where data quality and availability can vary significantly. These limitations suggest several promising directions for future research. Incorporating advanced transfer learning techniques could enhance the framework's ability to adapt to novel attack patterns with minimal retraining. Investigating more efficient network architectures and optimization techniques could reduce computational overhead while maintaining performance. The integration of active learning mechanisms could help address the challenge of limited labeled data in real-world deployments. Additionally, extending the framework to handle multi-agent scenarios where multiple security systems need to coordinate their responses could significantly enhance its utility in complex network environments. Exploring the incorporation of explainable AI techniques could improve transparency and trust in the system's decision-making process, making it more suitable for deployment in regulated environments. Finally, investigating methods for continuous online learning while maintaining system stability could enable the framework to evolve more effectively with emerging threats. The potential applications of ARCS extend beyond traditional cybersecurity scenarios. The framework's approach to balancing immediate response with strategic planning could be adapted to other domains requiring real-time decision making under uncertainty, such as network resource allocation, industrial control systems, and autonomous vehicle navigation. These broader applications suggest that the principles underlying ARCS could contribute to the development of more resilient and adaptive automated decision-making systems across various fields. Methodology, S.R. and J.J.; Software, S.R. ; Formal analysis, J.J.; Writing--original draft, S.R. ; Writing--review & editing, J.J. and G.N. ; Supervision, Y.L.