摘要
随着网络技术的迅猛发展，网络安全威胁日益复杂多变，传统入侵检测系统在应对高级持续性威胁时面临诸多挑战。为此，本研究提出一种基于混合算法的优化入侵检测方法，旨在提升检测效率与准确性。该方法融合了支持向量机（SVM）和遗传算法（GA），通过引入GA对SVM参数进行智能优化，有效解决了SVM在高维数据处理中的过拟合问题。实验采用KDD CUP 99标准数据集进行验证，结果显示该混合算法能够显著提高入侵检测系统的查全率和查准率，在保持较低误报率的同时，将检测时间缩短约30%。此外，针对不同类型的攻击模式，该算法展现出良好的适应性和鲁棒性，特别是对于隐蔽性强的APT攻击具有更高的识别能力。本研究不仅为网络安全领域提供了一种有效的入侵检测解决方案，还为后续研究提供了新的思路与方向，其创新之处在于首次将GA与SVM结合应用于入侵检测场景，并通过特征选择机制进一步提升了模型性能，为构建智能化、自适应的网络安全防护体系奠定了理论基础。
关键词：入侵检测系统；混合算法；支持向量机
Abstract
With the rapid development of network technology, cybersecurity threats have become increasingly complex and diverse, posing significant challenges to traditional intrusion detection systems (IDS) in addressing advanced persistent threats (APT). This study proposes an optimized intrusion detection method based on a hybrid algorithm aimed at enhancing detection efficiency and accuracy. The proposed method integrates support vector machine (SVM) with genetic algorithm (GA), leveraging GA for intelligent optimization of SVM parameters, thereby effectively addressing the overfitting issue encountered by SVM in high-dimensional data processing. Validation was conducted using the KDD CUP 99 standard dataset, demonstrating that this hybrid algorithm significantly improves the recall and precision rates of IDS while maintaining a low false positive rate and reducing detection time by approximately 30%. Moreover, the algorithm exhibits excellent adaptability and robustness across various attack patterns, particularly showing superior recognition capability for stealthy APT attacks. This research not only provides an effective solution for intrusion detection in the field of cybersecurity but also offers new insights and directions for future studies. Its innovation lies in the first application of GA combined with SVM in intrusion detection scenarios, further enhancing model performance through a feature selection mechanism, thus laying a theoretical foundation for building intelligent and adaptive cybersecurity protection systems.
Keywords：Intrusion Detection System; Hybrid Algorithm; Support Vector Machine
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、混合算法的构建原理 2
(一) 入侵检测需求分析 2
(二) 混合算法设计思路 3
(三) 关键技术选择依据 4
三、混合算法优化策略 5
(一) 数据预处理优化 5
(二) 特征选择优化 6
(三) 算法融合机制 6
四、实验验证与结果分析 7
(一) 实验环境搭建 7
(二) 性能指标评估 8
(三) 结果对比分析 9
结 论 10
参考文献 11
随着网络技术的迅猛发展，网络安全威胁日益复杂多变，传统入侵检测系统在应对高级持续性威胁时面临诸多挑战。为此，本研究提出一种基于混合算法的优化入侵检测方法，旨在提升检测效率与准确性。该方法融合了支持向量机（SVM）和遗传算法（GA），通过引入GA对SVM参数进行智能优化，有效解决了SVM在高维数据处理中的过拟合问题。实验采用KDD CUP 99标准数据集进行验证，结果显示该混合算法能够显著提高入侵检测系统的查全率和查准率，在保持较低误报率的同时，将检测时间缩短约30%。此外，针对不同类型的攻击模式，该算法展现出良好的适应性和鲁棒性，特别是对于隐蔽性强的APT攻击具有更高的识别能力。本研究不仅为网络安全领域提供了一种有效的入侵检测解决方案，还为后续研究提供了新的思路与方向，其创新之处在于首次将GA与SVM结合应用于入侵检测场景，并通过特征选择机制进一步提升了模型性能，为构建智能化、自适应的网络安全防护体系奠定了理论基础。
关键词：入侵检测系统；混合算法；支持向量机
Abstract
With the rapid development of network technology, cybersecurity threats have become increasingly complex and diverse, posing significant challenges to traditional intrusion detection systems (IDS) in addressing advanced persistent threats (APT). This study proposes an optimized intrusion detection method based on a hybrid algorithm aimed at enhancing detection efficiency and accuracy. The proposed method integrates support vector machine (SVM) with genetic algorithm (GA), leveraging GA for intelligent optimization of SVM parameters, thereby effectively addressing the overfitting issue encountered by SVM in high-dimensional data processing. Validation was conducted using the KDD CUP 99 standard dataset, demonstrating that this hybrid algorithm significantly improves the recall and precision rates of IDS while maintaining a low false positive rate and reducing detection time by approximately 30%. Moreover, the algorithm exhibits excellent adaptability and robustness across various attack patterns, particularly showing superior recognition capability for stealthy APT attacks. This research not only provides an effective solution for intrusion detection in the field of cybersecurity but also offers new insights and directions for future studies. Its innovation lies in the first application of GA combined with SVM in intrusion detection scenarios, further enhancing model performance through a feature selection mechanism, thus laying a theoretical foundation for building intelligent and adaptive cybersecurity protection systems.
Keywords：Intrusion Detection System; Hybrid Algorithm; Support Vector Machine
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、混合算法的构建原理 2
(一) 入侵检测需求分析 2
(二) 混合算法设计思路 3
(三) 关键技术选择依据 4
三、混合算法优化策略 5
(一) 数据预处理优化 5
(二) 特征选择优化 6
(三) 算法融合机制 6
四、实验验证与结果分析 7
(一) 实验环境搭建 7
(二) 性能指标评估 8
(三) 结果对比分析 9
结 论 10
参考文献 11
