摘要
随着信息技术的迅猛发展，网络安全问题日益凸显，传统优化方法在应对复杂网络环境下的安全挑战时存在局限性。为此，本文提出一种改进的遗传算法应用于网络安全优化，旨在提高网络系统的安全性与稳定性。该算法通过引入自适应交叉变异概率机制，在保持种群多样性的同时加速收敛速度；结合精英保留策略确保优质个体得以传承，增强算法全局搜索能力。将此改进算法用于检测并防御入侵行为、优化防火墙规则配置等场景。实验结果表明，相较于标准遗传算法及其他常见优化算法，所提方法能够更高效地发现潜在威胁，显著降低误报率与漏报率，优化后的防火墙规则有效提升了网络资源利用率。本研究创新性地融合了遗传算法优势与网络安全需求特点，为解决网络安全优化难题提供了新思路，不仅丰富了遗传算法的应用领域，也为构建更加智能可靠的网络安全防护体系奠定了理论基础。
关键词：网络安全优化；遗传算法；自适应交叉变异
Abstract
With the rapid development of information technology, cybersecurity issues have become increasingly prominent, and traditional optimization methods exhibit limitations in addressing security challenges in complex network environments. This paper proposes an improved genetic algorithm for cybersecurity optimization to enhance the security and stability of network systems. The proposed algorithm introduces an adaptive crossover and mutation probability mechanism to maintain population diversity while accelerating convergence speed, and incorporates an elitist preservation strategy to ensure the inheritance of high-quality individuals, thereby enhancing the global search capability of the algorithm. This improved algorithm is applied to scenarios such as detecting and defending against intrusion behaviors and optimizing firewall rule configurations. Experimental results demonstrate that, compared with standard genetic algorithms and other common optimization algorithms, the proposed method can more efficiently identify potential threats, significantly reducing false positive and false negative rates, and the optimized firewall rules effectively improve network resource utilization. This study innovatively integrates the advantages of genetic algorithms with the characteristics of cybersecurity requirements, providing new insights into solving cybersecurity optimization problems. It not only enriches the application domain of genetic algorithms but also lays a theoretical foundation for building a smarter and more reliable cybersecurity protection system.
Keywords：Network Security Optimization; Genetic Algorithm; Adaptive Crossover Mutation
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) 本文研究方法 2
二、改进遗传算法的设计 2
(一) 遗传算法基本原理 2
(二) 改进策略分析 3
(三) 改进算法的实现 4
三、网络安全优化模型构建 4
(一) 网络安全需求分析 5
(二) 模型框架设计 5
(三) 关键参数确定 6
四、应用案例与效果评估 7
(一) 实验环境搭建 7
(二) 案例应用分析 7
(三) 性能评估与结果 8
结 论 10
参考文献 11
随着信息技术的迅猛发展，网络安全问题日益凸显，传统优化方法在应对复杂网络环境下的安全挑战时存在局限性。为此，本文提出一种改进的遗传算法应用于网络安全优化，旨在提高网络系统的安全性与稳定性。该算法通过引入自适应交叉变异概率机制，在保持种群多样性的同时加速收敛速度；结合精英保留策略确保优质个体得以传承，增强算法全局搜索能力。将此改进算法用于检测并防御入侵行为、优化防火墙规则配置等场景。实验结果表明，相较于标准遗传算法及其他常见优化算法，所提方法能够更高效地发现潜在威胁，显著降低误报率与漏报率，优化后的防火墙规则有效提升了网络资源利用率。本研究创新性地融合了遗传算法优势与网络安全需求特点，为解决网络安全优化难题提供了新思路，不仅丰富了遗传算法的应用领域，也为构建更加智能可靠的网络安全防护体系奠定了理论基础。
关键词：网络安全优化；遗传算法；自适应交叉变异
Abstract
With the rapid development of information technology, cybersecurity issues have become increasingly prominent, and traditional optimization methods exhibit limitations in addressing security challenges in complex network environments. This paper proposes an improved genetic algorithm for cybersecurity optimization to enhance the security and stability of network systems. The proposed algorithm introduces an adaptive crossover and mutation probability mechanism to maintain population diversity while accelerating convergence speed, and incorporates an elitist preservation strategy to ensure the inheritance of high-quality individuals, thereby enhancing the global search capability of the algorithm. This improved algorithm is applied to scenarios such as detecting and defending against intrusion behaviors and optimizing firewall rule configurations. Experimental results demonstrate that, compared with standard genetic algorithms and other common optimization algorithms, the proposed method can more efficiently identify potential threats, significantly reducing false positive and false negative rates, and the optimized firewall rules effectively improve network resource utilization. This study innovatively integrates the advantages of genetic algorithms with the characteristics of cybersecurity requirements, providing new insights into solving cybersecurity optimization problems. It not only enriches the application domain of genetic algorithms but also lays a theoretical foundation for building a smarter and more reliable cybersecurity protection system.
Keywords：Network Security Optimization; Genetic Algorithm; Adaptive Crossover Mutation
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) 本文研究方法 2
二、改进遗传算法的设计 2
(一) 遗传算法基本原理 2
(二) 改进策略分析 3
(三) 改进算法的实现 4
三、网络安全优化模型构建 4
(一) 网络安全需求分析 5
(二) 模型框架设计 5
(三) 关键参数确定 6
四、应用案例与效果评估 7
(一) 实验环境搭建 7
(二) 案例应用分析 7
(三) 性能评估与结果 8
结 论 10
参考文献 11
