###### Abstract  
The increasing sophistication and frequency of cyber attacks necessitate automated and intelligent response mechanisms that can adapt to evolving threats. This paper presents ARCS (Adaptive Reinforcement learning for Cybersecurity Strategy), a novel framework that leverages deep reinforcement learning to optimize automated incident response strategies in cybersecurity systems. Our approach uniquely combines state representation learning of security events with a hierarchical decision-making process to map attack patterns to optimal defense measures. The framework employs a custom reward mechanism that balances incident resolution time, system stability, and defense effectiveness. Using a comprehensive dataset of 20,000 cybersecurity incidents, we demonstrate that ARCS achieves 27.3% faster incident resolution times and 31.2% higher defense effectiveness compared to traditional rule-based approaches. The framework shows particular strength in handling complex, multi-stage attacks, reducing false positive rates by 42.8% while maintaining robust system performance. Through extensive experiments, we validated that our approach can effectively generalize across different attack types and adapt to previously unseen threat patterns. The results suggest that reinforcement learning-based automation can significantly enhance cybersecurity incident response capabilities, particularly in environments requiring rapid and precise defensive actions. e 2025 1 9  
insufficient against advanced persistent threats. Recent advancements in machine learning (ML) and deep learning (DL) have introduced more robust techniques for anomaly detection and pattern recognition [4]. Notable studies have demonstrated significant progress in applying deep reinforcement learning for attack detection and mitigation in cyber-physical systems [5; 6; 7]. Concurrent developments have seen the successful application of natural language processing techniques to identify patterns and emerging threats in cybersecurity event logs [8; 9]. However, existing approaches face several critical limitations that impede their effectiveness in real-world deployments. Rule-based systems inherently lack the adaptability required to detect and respond to zero-day threats and evolving attack patterns. While ML and DL methods offer improved detection capabilities, they often require extensive labeled datasets and impose significant computational overhead [6; 10; 11]. Furthermore, systems employing static models frequently fail to dynamically respond to changing attack patterns, creating substantial gaps in real-time defense capabilities. The persistent challenges of high false-positive rates and limited generalization to unseen attack types further underscore the need for more sophisticated solutions [12; 13]. These limitations highlight the pressing need for an advanced cybersecurity framework that seamlessly integrates adaptability, intelligence, and scalability. The growing sophistication of cyber threats, coupled with the increasing complexity of digital infrastructure, demands a solution that can learn and adapt in real-time while maintaining operational efficiency. This motivation drives our research toward developing an adaptive reinforcement learning-based approach that can dynamically learn from and respond to emerging cyber threats [14; 15]. In this paper, we propose ARCS (Adaptive Reinforcement learning for Cybersecurity Strategy), a novel framework that leverages deep reinforcement learning to optimize automated incident response strategies in cybersecurity systems. ARCS distinguishes itself through several key innovations: (1) a hierarchical decision-making architecture that efficiently maps complex attack patterns to optimal defense measures, (2) an adaptive state representation learning mechanism that captures temporal dependencies in attack sequences, and (3) a multi-objective reward mechanism that balances incident resolution time, system stability, and defense effectiveness.