摘 要
随着信息技术的迅猛发展，网络恶意软件对信息安全构成了严重威胁，传统基于特征码的检测方法难以应对日益复杂的恶意软件变种。为此，本研究旨在构建一种基于行为分析的网络恶意软件检测方法，以提高检测准确性和实时性。该方法通过收集和分析恶意软件在网络环境中的行为特征，包括文件操作、注册表访问、网络连接等多维度数据，建立行为特征库，并利用机器学习算法进行训练，形成检测模型。创新之处在于融合了动态行为监测与静态代码分析，实现了对未知恶意软件的有效识别。实验结果表明，该方法能够显著提升恶意软件检测率，降低误报率，尤其在面对新型或变种恶意软件时表现出色。通过对大量样本的测试验证，证明了该方法具有较高的鲁棒性和泛化能力，为网络安全防护提供了新的思路和技术手段，有助于增强网络空间的安全保障体系，主要贡献在于突破了传统检测技术的局限，为恶意软件检测领域引入了更加智能和全面的行为分析机制。
关 键 词：恶意软件检测，行为分析，机器学习
ABSTRACT
With the rapid development of information technology, network malware poses a serious threat to information security, and traditional signature-based detection methods are difficult to cope with the increasingly complex varieties of malware. To this end, this study aims to construct a network malware detection method based on behavior analysis to improve detection accuracy and real-time performance. By collecting and analyzing the behavioral characteristics of malware in the network environment, including file operation, registry access, and network connection, the method establishes a behavioral feature library, and uses machine learning algorithms to form a detection model. The innovation lies in the integration of dynamic behavior monitoring and static code analysis, to achieve the effective identification of unknown malware. The experimental results show that this method can significantly improve the detection rate of malware and reduce the false alarm rate, especially in the face of new or variant malware. Through the test of a large number of samples, proved that the method has high robustness and generalization ability, provides a new idea for network security protection and technical means, help to enhance the security system of cyberspace, the main contribution is to break through the limitations of the traditional detection technology, for the field of malicious software detection introduced a more intelligent and comprehensive behavior analysis mechanism.
KEY WORDS：Malware detection, behavior analytics, machine learning
目 录
第1章 绪论 1
1.1 研究背景与意义 1
1.2 国内外研究现状综述 1
1.3 本文研究方法概述 2
第2章 行为特征提取与分析 3
2.1 恶意软件行为模式识别 3
2.2 动态行为特征提取技术 4
2.3 行为特征向量构建方法 4
第3章 检测模型与算法设计 6
3.1 基于机器学习的检测模型 6
3.2 实时检测算法优化策略 6
3.3 模型评估与性能分析 7
第4章 系统实现与应用验证 9
4.1 检测系统架构设计 9
4.2 实验环境与数据集构建 9
4.3 应用案例与效果评估 10
结 论 12
参考文献 13
致 谢 14
随着信息技术的迅猛发展，网络恶意软件对信息安全构成了严重威胁，传统基于特征码的检测方法难以应对日益复杂的恶意软件变种。为此，本研究旨在构建一种基于行为分析的网络恶意软件检测方法，以提高检测准确性和实时性。该方法通过收集和分析恶意软件在网络环境中的行为特征，包括文件操作、注册表访问、网络连接等多维度数据，建立行为特征库，并利用机器学习算法进行训练，形成检测模型。创新之处在于融合了动态行为监测与静态代码分析，实现了对未知恶意软件的有效识别。实验结果表明，该方法能够显著提升恶意软件检测率，降低误报率，尤其在面对新型或变种恶意软件时表现出色。通过对大量样本的测试验证，证明了该方法具有较高的鲁棒性和泛化能力，为网络安全防护提供了新的思路和技术手段，有助于增强网络空间的安全保障体系，主要贡献在于突破了传统检测技术的局限，为恶意软件检测领域引入了更加智能和全面的行为分析机制。
关 键 词：恶意软件检测，行为分析，机器学习
ABSTRACT
With the rapid development of information technology, network malware poses a serious threat to information security, and traditional signature-based detection methods are difficult to cope with the increasingly complex varieties of malware. To this end, this study aims to construct a network malware detection method based on behavior analysis to improve detection accuracy and real-time performance. By collecting and analyzing the behavioral characteristics of malware in the network environment, including file operation, registry access, and network connection, the method establishes a behavioral feature library, and uses machine learning algorithms to form a detection model. The innovation lies in the integration of dynamic behavior monitoring and static code analysis, to achieve the effective identification of unknown malware. The experimental results show that this method can significantly improve the detection rate of malware and reduce the false alarm rate, especially in the face of new or variant malware. Through the test of a large number of samples, proved that the method has high robustness and generalization ability, provides a new idea for network security protection and technical means, help to enhance the security system of cyberspace, the main contribution is to break through the limitations of the traditional detection technology, for the field of malicious software detection introduced a more intelligent and comprehensive behavior analysis mechanism.
KEY WORDS：Malware detection, behavior analytics, machine learning
目 录
第1章 绪论 1
1.1 研究背景与意义 1
1.2 国内外研究现状综述 1
1.3 本文研究方法概述 2
第2章 行为特征提取与分析 3
2.1 恶意软件行为模式识别 3
2.2 动态行为特征提取技术 4
2.3 行为特征向量构建方法 4
第3章 检测模型与算法设计 6
3.1 基于机器学习的检测模型 6
3.2 实时检测算法优化策略 6
3.3 模型评估与性能分析 7
第4章 系统实现与应用验证 9
4.1 检测系统架构设计 9
4.2 实验环境与数据集构建 9
4.3 应用案例与效果评估 10
结 论 12
参考文献 13
致 谢 14
