基于深度强化学习的油气企业网络安全动态防御模型研究

Research on a Dynamic Defense Model for Cybersecurity in Oil and Gas Enterprises Based on Deep Reinforcement Learning

摘要: 随着数字化转型在能源领域的纵深推进，石油天然气企业的网络安全防护体系正面临攻击手段智能化、威胁态势复杂化的严峻挑战。传统基于规则与特征匹配的静态防御机制已难以应对快速演变的网络威胁。本研究针对油气行业网络安全防护的现实痛点，创新性地将深度强化学习技术引入网络安全防御领域，提出了一种基于深度Q网络(DQN)算法的自适应智能防御模型。该模型通过与仿真网络环境的持续交互学习，实现了对异常行为模式的自主识别与动态响应策略优化。实验验证结果显示，所提模型在零日攻击检测准确率、误报率控制以及威胁响应时效等关键性能指标上均显著优于传统入侵检测系统，为石油天然气企业构建具备自主学习能力与环境适应能力的新一代智能化网络安全防护体系提供了理论基础与技术路径，具有重要的工程应用价值与产业推广前景。

Abstract: As the energy sector undergoes digital transformation, cybersecurity defence systems for oil and gas enterprises are facing severe challenges posed by increasingly sophisticated attack methods and complex threat landscapes. Traditional static defence mechanisms based on rules and signature matching are struggling to counter rapidly evolving cyber threats. Addressing critical pain points in oil and gas cybersecurity, this study integrates deep reinforcement learning into defence systems. It proposes an adaptive, intelligent defence model based on the Deep Q-Network (DQN) algorithm. Through continuous interactive learning with simulated network environments, the model autonomously identifies anomalous behaviour patterns and dynamically optimises response strategies. Experimental validation shows that the proposed model significantly outperforms tradi-tional intrusion detection systems in terms of key performance metrics such as zero-day attack de-tection accuracy, control of the false positive rate, and timeliness of threat response. This provides a theoretical foundation and technical pathway for oil and gas enterprises to develop a new genera-tion of intelligent cybersecurity defence systems with autonomous learning and environmental adaptability capabilities. The model has significant engineering application value and promising industrial promotion prospects.