摘要
随着信息技术的迅猛发展，网络安全威胁日益复杂多变，传统防御策略难以应对高级持续性威胁等新型攻击。为此，本研究聚焦深度强化学习在网络安全防御策略中的优化应用，旨在构建一种自适应、智能化的防御体系。通过引入深度强化学习算法，将网络环境建模为马尔可夫决策过程，以状态空间、动作空间和奖励函数为核心要素，设计出能够实时感知网络态势并动态调整防御措施的智能体。该智能体利用神经网络强大的表征能力对高维复杂的网络流量数据进行特征提取与抽象表示，并借助强化学习机制不断探索最优防御策略，在模拟环境中经过大量迭代训练后，实现了对未知威胁的有效检测与响应。实验结果表明，基于深度强化学习的防御策略相较于传统方法具有更高的检测率和更低的误报率，尤其在面对零日攻击时展现出显著优势。此外，本研究还提出了一种新颖的多智能体协作框架，允许多个防御节点之间共享信息并协同作战，进一步提升了整体防御效能，为网络安全领域提供了创新性的解决方案。
关键词：深度强化学习；网络安全防御；马尔可夫决策过程
Abstract
With the rapid development of information technology, network security threats are increasingly complex and changeable, and traditional defense strategies are difficult to deal with new attacks such as advanced persistent threats. To this end, this study focuses on the optimal application of deep reinforcement learning in network security defense strategies, aiming to build an adaptive and intelligent defense system. By introducing a deep reinforcement learning algorithm, the network environment is modeled as a Markov decision process, with the state space, action space and reward function as the core elements, designing agents that can sense the network situation in real time and dynamically adjust the defense measures. The agent uses the powerful representation ability of the neural network to extract and abstract the features of high-dimensional complex network traffic data, and constantly explores the optimal defense strategy with the help of reinforcement learning mechanism. After a lot of iterative training in the simulation environment, it realizes the effective detection and response of unknown threats. The experimental results show that the defense strategy based on deep reinforcement learning has higher detection rate and lower false positive rate than the traditional methods, and shows significant advantages especially in the face of zero-day attacks. In addition, this study presents a novel multi-agent collaboration fr amework that allows multiple defense nodes to share information and work together, further enhancing the overall defense effectiveness and providing innovative solutions in the field of network security.
Keywords：Deep Reinforcement Learning; Cyber Security Defense; Markov Decision Process
目 录
摘要 I
Abstract II
一、绪论 1
(一) 研究背景与意义 1
(二) 国内外研究现状 1
(三) 研究方法概述 2
二、深度强化学习基础理论 2
(一) 深度学习基本原理 2
(二) 强化学习核心机制 3
(三) 深度强化学习融合方式 4
三、网络安全防御需求分析 4
(一) 网络威胁特征识别 5
(二) 防御策略关键要素 5
(三) 安全防护体系架构 6
四、深度强化学习优化应用 7
(一) 攻击检测模型构建 7
(二) 防御决策智能生成 8
(三) 策略效果评估体系 8
结 论 10
参考文献 11
随着信息技术的迅猛发展，网络安全威胁日益复杂多变，传统防御策略难以应对高级持续性威胁等新型攻击。为此，本研究聚焦深度强化学习在网络安全防御策略中的优化应用，旨在构建一种自适应、智能化的防御体系。通过引入深度强化学习算法，将网络环境建模为马尔可夫决策过程，以状态空间、动作空间和奖励函数为核心要素，设计出能够实时感知网络态势并动态调整防御措施的智能体。该智能体利用神经网络强大的表征能力对高维复杂的网络流量数据进行特征提取与抽象表示，并借助强化学习机制不断探索最优防御策略，在模拟环境中经过大量迭代训练后，实现了对未知威胁的有效检测与响应。实验结果表明，基于深度强化学习的防御策略相较于传统方法具有更高的检测率和更低的误报率，尤其在面对零日攻击时展现出显著优势。此外，本研究还提出了一种新颖的多智能体协作框架，允许多个防御节点之间共享信息并协同作战，进一步提升了整体防御效能，为网络安全领域提供了创新性的解决方案。
关键词：深度强化学习；网络安全防御；马尔可夫决策过程
Abstract
With the rapid development of information technology, network security threats are increasingly complex and changeable, and traditional defense strategies are difficult to deal with new attacks such as advanced persistent threats. To this end, this study focuses on the optimal application of deep reinforcement learning in network security defense strategies, aiming to build an adaptive and intelligent defense system. By introducing a deep reinforcement learning algorithm, the network environment is modeled as a Markov decision process, with the state space, action space and reward function as the core elements, designing agents that can sense the network situation in real time and dynamically adjust the defense measures. The agent uses the powerful representation ability of the neural network to extract and abstract the features of high-dimensional complex network traffic data, and constantly explores the optimal defense strategy with the help of reinforcement learning mechanism. After a lot of iterative training in the simulation environment, it realizes the effective detection and response of unknown threats. The experimental results show that the defense strategy based on deep reinforcement learning has higher detection rate and lower false positive rate than the traditional methods, and shows significant advantages especially in the face of zero-day attacks. In addition, this study presents a novel multi-agent collaboration fr amework that allows multiple defense nodes to share information and work together, further enhancing the overall defense effectiveness and providing innovative solutions in the field of network security.
Keywords：Deep Reinforcement Learning; Cyber Security Defense; Markov Decision Process
目 录
摘要 I
Abstract II
一、绪论 1
(一) 研究背景与意义 1
(二) 国内外研究现状 1
(三) 研究方法概述 2
二、深度强化学习基础理论 2
(一) 深度学习基本原理 2
(二) 强化学习核心机制 3
(三) 深度强化学习融合方式 4
三、网络安全防御需求分析 4
(一) 网络威胁特征识别 5
(二) 防御策略关键要素 5
(三) 安全防护体系架构 6
四、深度强化学习优化应用 7
(一) 攻击检测模型构建 7
(二) 防御决策智能生成 8
(三) 策略效果评估体系 8
结 论 10
参考文献 11
