摘 要
随着网络技术的迅猛发展，恶意软件的传播速度和变种数量显著增加，对网络安全构成了严重威胁，传统的基于特征码的检测方法已难以应对日益复杂的恶意软件环境因此，本研究旨在探索支持向量机（SVM）在恶意软件检测中的应用潜力，以期提供一种高效、准确且具备泛化能力的检测方案研究中采用SVM作为核心分类算法，结合静态分析与动态行为特征提取技术，构建了多层次的特征选择机制通过对比不同核函数的性能表现，并引入参数优化策略以提升模型的分类精度实验结果表明，基于SVM的恶意软件检测方法在处理高维特征数据时表现出优异的分类能力，其检测准确率相较于传统方法提升了约15%，同时具备较强的鲁棒性和抗干扰能力此外，本研究创新性地提出了一种融合静态与动态特征的混合特征表示方法，有效解决了单一特征空间信息不足的问题，从而进一步增强了模型的检测效果综上所述，支持向量机在恶意软件检测领域具有显著优势，为未来智能化网络安全防护体系的构建提供了重要参考。
关键词：支持向量机，恶意软件检测，静态分析
ABSTRACT
With the rapid development of network technology, the spread speed and number of variants of malware have increased significantly, posing a serious threat to network security. Traditional detection methods based on feature codes have been unable to cope with the increasingly complex malware environment. Therefore, this study aims to explore the application potential of support vector machine (SVM) in malware detection. In order to provide an efficient and accurate detection scheme with generalization ability, SVM is adopted as the core classification algorithm in this study, and a multi-level feature selection mechanism is constructed by combining static analysis and dynamic behavior feature extraction techniques. By comparing the performance of different kernel functions, parameter optimization strategies are introduced to improve the classification accuracy of the model. The SVM-based malware detection method shows excellent classification ability when processing high-dimensional feature data, its detection accuracy is improved by about 15% compared with traditional methods, and it also has strong robustness and anti-interference ability. In addition, this research innovatively proposes a hybrid feature representation method that integrates static and dynamic features. It effectively solves the problem of insufficient spatial information of a single feature, thus further enhancing the detection effect of the model. In summary, support vector machine has significant advantages in the field of malware detection, and provides an important reference for the construction of intelligent network security protection system in the future.
Keywords: Support vector machine, malware detection, static analysis
目 录
摘 要 I
ABSTRACT II
第一章 绪论 1
1.1 研究背景及意义 1
1.2 研究意义与价值分析 1
1.3 国内外研究现状综述 2
1.4 本文研究方法与技术路线 2
第二章 支持向量机理论基础 3
2.1 支持向量机基本原理 3
2.2 核函数选择及其影响 3
2.3 数据预处理方法研究 4
2.4 特征提取与降维技术 4
2.5 支持向量机优化策略 5
第三章 恶意软件检测关键技术 6
3.1 恶意软件特征分析方法 6
3.2 基于支持向量机的分类模型构建 6
3.3 动态与静态特征融合研究 6
3.4 不平衡数据集处理方法 7
3.5 性能评估指标体系设计 7
第四章 实验设计与结果分析 8
4.1 实验环境与数据集介绍 8
4.2 参数调优与模型训练过程 8
4.3 实验结果对比与分析 8
4.4 错误检测原因探讨 9
4.5 改进方向与未来展望 9
结束语 11
谢 辞 12
参考文献 13
