Research on Reinforcement Learning-Based Approaches for Real-Time Defense Against Network Attacks

As cybersecurity threats continue to evolve, it has become evident that traditional defence mechanisms are unable to cope with the increasingly sophisticated and volatile cyberattacks that are becoming more prevalent. This paper puts forward a novel real-time network attack defence model based on reinforcement learning, which represents a significant departure from the existing intrusion detection and defence systems. This method employs deep reinforcement learning (DRL) to facilitate autonomous and adaptive decision-making, enabling the dynamic adjustment of network defence strategies in accordance with real-time network traffic and attack patterns. The proposed model combines a multi-layer neural network architecture and a bespoke reward mechanism to achieve an equilibrium between the efficacy of the defence mechanism and the utilisation of resources. It is capable of detecting and mitigating real-time attacks by fusing the temporal and spatial characteristics of network traffic. The experimental results demonstrate that the proposed model exhibits superior performance in terms of attack detection accuracy, response time and network throughput when compared to both the traditional rule-based defence system and the existing reinforcement learning defence framework.