摘 要
随着信息技术的快速发展，恶意软件的威胁日益严重，传统基于特征码的检测方法已难以应对不断演化的新型攻击手段。为此，本研究提出了一种基于机器学习的恶意软件检测与分类方法，旨在通过先进的算法模型提升检测效率和准确性。研究首先构建了一个包含静态特征和动态行为特征的多维度特征集，并采用特征选择技术优化特征空间，以降低计算复杂度并提高模型泛化能力。在此基础上，设计了融合支持向量机、随机森林及深度神经网络的集成学习框架，用于实现对恶意软件的高效检测与精准分类。实验结果表明，该方法在公开数据集上的检测准确率达到了98.7%，相较于单一模型方法具有显著优势，同时在未知样本检测中表现出较强的鲁棒性。此外，本研究还提出了一种自适应权重调整机制，可根据实际应用场景动态优化模型性能，进一步提升了系统的实用性。总体而言，本研究不仅为恶意软件检测提供了新的技术路径，还在特征工程与模型优化方面做出了重要贡献，为未来相关领域的研究奠定了坚实基础。
关键词：恶意软件检测；机器学习；特征选择；集成学习；自适应权重调整
Abstract
With the rapid development of information technology, the threat of malware has become increasingly severe, and traditional signature-based detection methods are struggling to cope with the constantly evolving attack techniques. To address this challenge, this study proposes a machine-learning-based approach for malware detection and classification, aiming to enhance detection efficiency and accuracy through advanced algorithmic models. A multi-dimensional feature set incorporating both static features and dynamic behavioral features was constructed, and feature selection techniques were employed to optimize the feature space, thereby reducing computational complexity and improving model generalization. On this basis, an ensemble learning fr amework integrating support vector machines, random forests, and deep neural networks was designed to achieve efficient malware detection and precise classification. Experimental results demonstrate that the proposed method achieves a detection accuracy of 98.7% on public datasets, showing significant advantages over single-model approaches, while exhibiting strong robustness in detecting unknown samples. Furthermore, this study introduces an adaptive weight adjustment mechanism that can dynamically optimize model performance according to real-world application scenarios, enhancing the system's practicality. Overall, this research not only provides a novel technical pathway for malware detection but also makes important contributions to feature engineering and model optimization, laying a solid foundation for future studies in related fields.
Keywords: Malware Detection; Machine Learning; Feature Selection; Ensemble Learning; Adaptive Weight Adjustment
目 录
1绪论 1
1.1恶意软件检测的研究背景与意义 1
1.2机器学习在恶意软件检测中的应用现状 1
1.3本文研究方法与技术路线 2
2数据特征提取与预处理方法 2
2.1恶意软件数据集的构建与选择 2
2.2特征提取技术及其优化策略 3
2.3数据预处理方法与质量评估 3
2.4特征降维对分类性能的影响分析 4
2.5数据增强技术在恶意软件检测中的应用 4
3机器学习算法在恶意软件检测中的应用 5
3.1常见机器学习算法的适用性分析 5
3.2监督学习在恶意软件分类中的实现 5
3.3非监督学习在异常检测中的作用 6
3.4深度学习模型在复杂特征捕捉中的优势 6
3.5算法性能比较与优化方向探讨 7
4检测系统设计与实验验证 7
4.1恶意软件检测系统的架构设计 7
4.2实验环境搭建与参数配置 8
4.3不同场景下的检测效果评估 8
4.4性能指标分析与结果讨论 9
4.5系统局限性及改进方向 10
结论 10
参考文献 12
致 谢 13
