摘 要
随着网络技术的快速发展，恶意软件的传播方式日益复杂化和隐蔽化，传统检测方法在面对新型威胁时逐渐显现出局限性，因此亟需一种高效、精准且适应性强的检测机制以应对不断演化的安全挑战本研究旨在构建基于贝叶斯网络的恶意软件检测模型，通过融合概率推理与机器学习技术，提升对未知恶意软件的识别能力研究中提出了一种改进型贝叶斯网络结构，结合特征选择算法优化输入变量，并引入动态更新机制以适应恶意软件行为模式的变化实验结果表明，该模型在检测准确率、召回率及运行效率等方面均优于传统签名匹配和静态规则检测方法，特别是在针对变种恶意软件的识别上表现出显著优势此外，模型具备较强的可扩展性，能够根据实际需求调整复杂度以平衡性能与资源消耗综上所述，本研究不仅为恶意软件检测提供了新的理论支持，还为后续相关领域的深入探索奠定了基础其创新点在于将动态行为特征与概率图模型相结合，有效提升了检测系统的智能化水平和泛化能力。
关键词：恶意软件检测，贝叶斯网络，动态行为特征，概率推理
ABSTRACT
With the rapid development of network technology, the transmission mode of malware is increasingly complicated and concealed. Traditional detection methods are gradually showing limitations in the face of new threats. Therefore, an efficient, accurate and adaptable detection mechanism is urgently needed to deal with the evolving security challenges. By combining probabilistic reasoning and machine learning technology, an improved Bayesian network structure is proposed in this study, which combines feature selection algorithm to optimize input variables, and introduces a dynamic update mechanism to adapt to changes in the behavior pattern of malware. The model is superior to traditional signature matching and static rule detection methods in terms of detection accuracy, recall rate and operation efficiency, especially in the identification of variant malware. In addition, the model has strong scalability and can adjust complexity according to actual needs to balance performance and resource consumption. This research not only provides new theoretical support for malware detection, but also lays a foundation for further exploration in related fields. Its innovation lies in the combination of dynamic behavior characteristics and probability graph model, which effectively improves the intelligence level and generalization ability of detection system.
Keywords: Malware detection, Bayesian networks, dynamic behavior characteristics, probabilistic reasoning
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 贝叶斯网络在恶意软件检测中的应用现状 1
第二章 贝叶斯网络基础理论分析 3
2.1 贝叶斯网络的基本概念与原理 3
2.2 条件概率与依赖关系建模 3
2.3 贝叶斯网络的结构学习方法 3
2.4 参数估计与推理算法研究 4
2.5 理论在恶意软件检测中的适用性 4
第三章 恶意软件特征提取与建模方法 5
3.1 恶意软件行为特征分析 5
3.2 特征选择与降维技术研究 5
3.3 基于贝叶斯网络的特征建模方法 6
3.4 数据集构建与实验设计 7
3.5 特征模型的验证与优化 7
第四章 检测模型设计与性能评估 9
4.1 检测模型的整体架构设计 9
4.2 贝叶斯网络参数调优策略 9
4.3 实验环境搭建与数据采集 9
4.4 模型性能评估指标体系 10
4.5 结果分析与改进方向 10
结束语 12
谢 辞 13
参考文献 14
