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，传统漏洞挖掘方法在面对新型攻击手段时逐渐暴露出效率低下、准确性不足等问题。为此，本文提出一种基于深度强化学习的智能网络安全漏洞挖掘算法，旨在提升漏洞检测的精准度与效率。该算法融合了深度神经网络强大的特征提取能力以及强化学习的决策优化机制，通过构建虚拟环境模拟真实网络场景，利用代理模型加速训练过程并提高泛化能力。实验结果表明，在多个公开数据集上，所提算法相较于传统方法能够更快速地定位潜在漏洞，平均检测时间缩短约35%，同时将误报率降低了28%。此外，该算法还具备自适应调整参数的能力，可根据不同应用场景动态优化性能表现。本研究不仅为网络安全领域提供了新的技术思路，也为后续相关研究奠定了理论基础，特别是在智能化安全防护体系建设方面具有重要应用价值。
关键词：深度强化学习 网络安全漏洞挖掘 智能算法
Abstract
With the rapid development of information technology, the network security threats are increasingly complex and changeable, and the traditional vulnerability mining methods gradually expose the problems such as low efficiency and insufficient accuracy in the face of new attack means. To this end, this paper proposes an intelligent network security vulnerability mining algorithm based on deep reinforcement learning, aiming to improve the accuracy and efficiency of vulnerability detection. The algorithm integrates the powerful feature extraction ability of the deep neural network and the decision optimization mechanism of reinforcement learning. It builds the virtual environment to simulate the real network scenes, and uses the agent model to accelerate the training process and improve the generalization ability. The experimental results show that, the proposed algorithm can locate potential vulnerabilities more quickly than the traditional method, reducing the average detection time by about 35% and reducing the false positive rate by 28%. In addition, the algorithm also has the ability to adjust parameters and can dynamically optimize the performance according to different application scenarios. This study not only provides new technical ideas for the field of network security, but also lays a theoretical foundation for the subsequent relevant research, especially in the construction of intelligent security protection system has important application value.
Keyword:Deep Reinforcement Learning Cybersecurity Vulnerability Mining Intelligent Algorithm
目 录
1绪论 1
1.1研究背景与意义 1
1.2国内外研究现状 1
1.3本文研究方法 2
2智能漏洞挖掘算法理论基础 2
2.1网络安全漏洞概述 2
2.2智能算法在漏洞挖掘中的应用 3
2.3关键技术分析 3
3智能漏洞挖掘算法设计 4
3.1算法框架构建 4
3.2核心模块设计 5
3.3数据处理与特征提取 5
4智能漏洞挖掘算法实现与评估 6
4.1实验环境搭建 6
4.2算法性能测试 7
4.3结果分析与讨论 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，传统漏洞挖掘方法在面对新型攻击手段时逐渐暴露出效率低下、准确性不足等问题。为此，本文提出一种基于深度强化学习的智能网络安全漏洞挖掘算法，旨在提升漏洞检测的精准度与效率。该算法融合了深度神经网络强大的特征提取能力以及强化学习的决策优化机制，通过构建虚拟环境模拟真实网络场景，利用代理模型加速训练过程并提高泛化能力。实验结果表明，在多个公开数据集上，所提算法相较于传统方法能够更快速地定位潜在漏洞，平均检测时间缩短约35%，同时将误报率降低了28%。此外，该算法还具备自适应调整参数的能力，可根据不同应用场景动态优化性能表现。本研究不仅为网络安全领域提供了新的技术思路，也为后续相关研究奠定了理论基础，特别是在智能化安全防护体系建设方面具有重要应用价值。
关键词：深度强化学习 网络安全漏洞挖掘 智能算法
Abstract
With the rapid development of information technology, the network security threats are increasingly complex and changeable, and the traditional vulnerability mining methods gradually expose the problems such as low efficiency and insufficient accuracy in the face of new attack means. To this end, this paper proposes an intelligent network security vulnerability mining algorithm based on deep reinforcement learning, aiming to improve the accuracy and efficiency of vulnerability detection. The algorithm integrates the powerful feature extraction ability of the deep neural network and the decision optimization mechanism of reinforcement learning. It builds the virtual environment to simulate the real network scenes, and uses the agent model to accelerate the training process and improve the generalization ability. The experimental results show that, the proposed algorithm can locate potential vulnerabilities more quickly than the traditional method, reducing the average detection time by about 35% and reducing the false positive rate by 28%. In addition, the algorithm also has the ability to adjust parameters and can dynamically optimize the performance according to different application scenarios. This study not only provides new technical ideas for the field of network security, but also lays a theoretical foundation for the subsequent relevant research, especially in the construction of intelligent security protection system has important application value.
Keyword:Deep Reinforcement Learning Cybersecurity Vulnerability Mining Intelligent Algorithm
目 录
1绪论 1
1.1研究背景与意义 1
1.2国内外研究现状 1
1.3本文研究方法 2
2智能漏洞挖掘算法理论基础 2
2.1网络安全漏洞概述 2
2.2智能算法在漏洞挖掘中的应用 3
2.3关键技术分析 3
3智能漏洞挖掘算法设计 4
3.1算法框架构建 4
3.2核心模块设计 5
3.3数据处理与特征提取 5
4智能漏洞挖掘算法实现与评估 6
4.1实验环境搭建 6
4.2算法性能测试 7
4.3结果分析与讨论 7
结论 8
参考文献 9
致谢 10
