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，传统静态检测方法难以应对动态变化的网络攻击。为此，本研究聚焦强化学习在动态网络安全检测中的应用，旨在构建一种自适应、智能化的网络安全检测系统。通过引入强化学习算法，使系统能够根据网络环境的变化自主学习最优策略，从而实现对新型未知威胁的有效检测。研究采用深度Q网络（DQN）作为核心算法框架，结合特征工程提取网络流量的关键属性，并设计奖励机制引导模型优化。实验结果表明，该方法在检测准确率方面较传统方法有显著提升，特别是在面对零日攻击等未知威胁时表现出更强的鲁棒性。此外，所提方案具备良好的泛化能力，能够在不同网络环境下保持稳定性能。
关键词：强化学习 网络安全检测 深度Q网络
Abstract
With the rapid development of information technology, the network security threats are increasingly complex and changeable, and the traditional static detection method is difficult to deal with the dynamically changing network attacks. Therefore, this study focuses on the application of reinforcement learning in dynamic network security detection, aiming to build an adaptive and intelligent network security detection system. By introducing the reinforcement learning algorithm, the system can independently learn the optimal strategy according to the changes of the network environment, so as to realize the effective detection of new unknown threats. In this study, deep Q network (DQN) is used as the core algorithm fr amework, extracting the key properties of network traffic combined with feature engineering, and designing the reward mechanism to guide the model optimization. The experimental results show that the proposed method is significantly improved in detection accuracy than traditional methods, especially showing stronger robustness against unknown threats such as zero-day attacks. In addition, the proposed scheme has good generalization ability and can maintain stable performance in different network environments.
Keyword:Reinforcement Learning Cybersecurity Detection Deep Q Network
目 录
1绪论 1
1.1强化学习与网络安全检测背景 1
1.2国内外研究现状综述 1
1.3本文研究方法概述 1
2强化学习算法在动态检测中的适应性 2
2.1动态网络环境特点分析 2
2.2强化学习算法选择依据 3
2.3算法适应性改进方案 3
3基于强化学习的入侵检测模型构建 4
3.1模型架构设计原则 4
3.2特征提取与状态表示 5
3.3奖励函数设计思路 5
4实验验证与结果分析 6
4.1实验环境搭建说明 6
4.2检测性能评估指标 7
4.3结果对比与分析讨论 7
结论 8
参考文献 9
致谢 10
随着信息技术的迅猛发展，网络安全威胁日益复杂多变，传统静态检测方法难以应对动态变化的网络攻击。为此，本研究聚焦强化学习在动态网络安全检测中的应用，旨在构建一种自适应、智能化的网络安全检测系统。通过引入强化学习算法，使系统能够根据网络环境的变化自主学习最优策略，从而实现对新型未知威胁的有效检测。研究采用深度Q网络（DQN）作为核心算法框架，结合特征工程提取网络流量的关键属性，并设计奖励机制引导模型优化。实验结果表明，该方法在检测准确率方面较传统方法有显著提升，特别是在面对零日攻击等未知威胁时表现出更强的鲁棒性。此外，所提方案具备良好的泛化能力，能够在不同网络环境下保持稳定性能。
关键词：强化学习 网络安全检测 深度Q网络
Abstract
With the rapid development of information technology, the network security threats are increasingly complex and changeable, and the traditional static detection method is difficult to deal with the dynamically changing network attacks. Therefore, this study focuses on the application of reinforcement learning in dynamic network security detection, aiming to build an adaptive and intelligent network security detection system. By introducing the reinforcement learning algorithm, the system can independently learn the optimal strategy according to the changes of the network environment, so as to realize the effective detection of new unknown threats. In this study, deep Q network (DQN) is used as the core algorithm fr amework, extracting the key properties of network traffic combined with feature engineering, and designing the reward mechanism to guide the model optimization. The experimental results show that the proposed method is significantly improved in detection accuracy than traditional methods, especially showing stronger robustness against unknown threats such as zero-day attacks. In addition, the proposed scheme has good generalization ability and can maintain stable performance in different network environments.
Keyword:Reinforcement Learning Cybersecurity Detection Deep Q Network
目 录
1绪论 1
1.1强化学习与网络安全检测背景 1
1.2国内外研究现状综述 1
1.3本文研究方法概述 1
2强化学习算法在动态检测中的适应性 2
2.1动态网络环境特点分析 2
2.2强化学习算法选择依据 3
2.3算法适应性改进方案 3
3基于强化学习的入侵检测模型构建 4
3.1模型架构设计原则 4
3.2特征提取与状态表示 5
3.3奖励函数设计思路 5
4实验验证与结果分析 6
4.1实验环境搭建说明 6
4.2检测性能评估指标 7
4.3结果对比与分析讨论 7
结论 8
参考文献 9
致谢 10
