摘 要
随着工业互联网的快速发展，网络异常检测成为保障工业系统安全稳定运行的关键技术。针对工业互联网环境下的复杂网络结构和多源异构数据特征，本文提出一种基于深度学习与图神经网络融合的网络异常检测模型。该模型旨在解决传统方法在处理高维、非线性数据时存在的局限性，通过构建图卷积网络对工业网络拓扑结构进行建模，结合长短期记忆网络提取时间序列特征，实现对网络流量的精准分析。实验结果表明，所提模型在多个工业场景下的异常检测准确率达到95%以上，相比现有方法提升了10% - 15%，且具有更强的鲁棒性和泛化能力。此外，该模型能够有效识别新型攻击模式，为工业互联网安全防护提供了新的思路和技术手段，其创新之处在于将图神经网络应用于工业网络拓扑结构建模，并融合时间序列分析，实现了对网络异常行为的高效检测，为工业互联网的安全监测提供了可靠的解决方案，对于推动工业互联网安全技术的发展具有重要意义。
关键词：工业互联网安全；网络异常检测；图神经网络
Abstract
With the rapid development of industrial internet, network anomaly detection has become a critical technology for ensuring the secure and stable operation of industrial systems. In response to the complex network structures and multi-source heterogeneous data characteristics in industrial internet environments, this paper proposes a novel network anomaly detection model that integrates deep learning with graph neural networks. The proposed model aims to address the limitations of traditional methods when dealing with high-dimensional and nonlinear data by constructing a graph convolutional network to model the topology of industrial networks and combining it with long short-term memory networks to extract time-series features, thereby achieving precise analysis of network traffic. Experimental results demonstrate that the proposed model achieves an anomaly detection accuracy exceeding 95% across multiple industrial scenarios, representing a 10%-15% improvement over existing methods, while also exhibiting stronger robustness and generalization capabilities. Moreover, the model effectively identifies novel attack patterns, providing new insights and technical approaches for the security protection of industrial internet. Its innovation lies in applying graph neural networks to the modeling of industrial network topologies and integrating time-series analysis, enabling efficient detection of network anomalies and offering a reliable solution for the security monitoring of industrial internet, which is of significant importance for advancing the development of industrial internet security technologies.
Keywords：Industrial Internet Security；Network Anomaly Detection；Graph Neural Network
目 录
引 言 1
第一章 工业互联网网络特性分析 2
1.1 工业互联网架构概述 2
1.2 网络流量特征提取 2
1.3 异常检测需求分析 3
第二章 异常检测模型构建方法 5
2.1 模型选择与评估指标 5
2.2 数据预处理技术 5
2.3 特征工程优化策略 6
第三章 异常检测算法研究 8
3.1 基于统计的检测算法 8
3.2 机器学习检测方法 8
3.3 深度学习检测框架 9
第四章 实验验证与结果分析 11
4.1 实验环境搭建 11
4.2 模型性能测试 11
4.3 结果对比与讨论 12
结 论 14
参考文献 15
致 谢 16
随着工业互联网的快速发展，网络异常检测成为保障工业系统安全稳定运行的关键技术。针对工业互联网环境下的复杂网络结构和多源异构数据特征，本文提出一种基于深度学习与图神经网络融合的网络异常检测模型。该模型旨在解决传统方法在处理高维、非线性数据时存在的局限性，通过构建图卷积网络对工业网络拓扑结构进行建模，结合长短期记忆网络提取时间序列特征，实现对网络流量的精准分析。实验结果表明，所提模型在多个工业场景下的异常检测准确率达到95%以上，相比现有方法提升了10% - 15%，且具有更强的鲁棒性和泛化能力。此外，该模型能够有效识别新型攻击模式，为工业互联网安全防护提供了新的思路和技术手段，其创新之处在于将图神经网络应用于工业网络拓扑结构建模，并融合时间序列分析，实现了对网络异常行为的高效检测，为工业互联网的安全监测提供了可靠的解决方案，对于推动工业互联网安全技术的发展具有重要意义。
关键词：工业互联网安全；网络异常检测；图神经网络
Abstract
With the rapid development of industrial internet, network anomaly detection has become a critical technology for ensuring the secure and stable operation of industrial systems. In response to the complex network structures and multi-source heterogeneous data characteristics in industrial internet environments, this paper proposes a novel network anomaly detection model that integrates deep learning with graph neural networks. The proposed model aims to address the limitations of traditional methods when dealing with high-dimensional and nonlinear data by constructing a graph convolutional network to model the topology of industrial networks and combining it with long short-term memory networks to extract time-series features, thereby achieving precise analysis of network traffic. Experimental results demonstrate that the proposed model achieves an anomaly detection accuracy exceeding 95% across multiple industrial scenarios, representing a 10%-15% improvement over existing methods, while also exhibiting stronger robustness and generalization capabilities. Moreover, the model effectively identifies novel attack patterns, providing new insights and technical approaches for the security protection of industrial internet. Its innovation lies in applying graph neural networks to the modeling of industrial network topologies and integrating time-series analysis, enabling efficient detection of network anomalies and offering a reliable solution for the security monitoring of industrial internet, which is of significant importance for advancing the development of industrial internet security technologies.
Keywords：Industrial Internet Security；Network Anomaly Detection；Graph Neural Network
目 录
引 言 1
第一章 工业互联网网络特性分析 2
1.1 工业互联网架构概述 2
1.2 网络流量特征提取 2
1.3 异常检测需求分析 3
第二章 异常检测模型构建方法 5
2.1 模型选择与评估指标 5
2.2 数据预处理技术 5
2.3 特征工程优化策略 6
第三章 异常检测算法研究 8
3.1 基于统计的检测算法 8
3.2 机器学习检测方法 8
3.3 深度学习检测框架 9
第四章 实验验证与结果分析 11
4.1 实验环境搭建 11
4.2 模型性能测试 11
4.3 结果对比与讨论 12
结 论 14
参考文献 15
致 谢 16
