摘 要
随着互联网的迅猛发展，网络流量呈现出复杂多变的特征，异常流量对网络安全构成严重威胁。为此，本研究聚焦于网络流量特征提取与异常检测技术，旨在构建一种高效、精准的异常检测系统。通过深入分析现有方法存在的问题，提出基于深度学习与传统统计方法相结合的混合模型，利用深度神经网络强大的特征学习能力自动提取流量特征，并结合统计方法对特定场景下的流量进行精细化建模。实验结果表明，该方法在准确率和召回率方面均优于传统单一方法，在多种真实网络环境中实现了98%以上的检测精度。此外，针对大规模数据处理需求，设计了分布式计算框架以提高算法效率。
关键词：网络流量异常检测 深度学习与统计融合 特征提取
Abstract
With the rapid development of the Internet, the network traffic presents the complex and changeable characteristics, and the abnormal traffic poses a serious threat to the network security. To this end, this study focuses on the network traffic feature extraction and anomaly detection technology, aiming to build an efficient and accurate anomaly detection system. Through the in-depth analysis of the problems of the existing methods, the hybrid model based on deep learning and traditional statistical methods is proposed, using the powerful feature learning ability of deep neural network to automatically extract the traffic features, and fine model the traffic in a specific scene in combination with the statistical method. Experimental results show that the proposed method outperforms the traditional single method in both accuracy and recall, and achieves detection accuracy above 98% in multiple real network environments. In addition, a distributed computing fr amework is designed to improve the efficiency of the algorithms for large-scale data processing requirements.
Keyword:Network Traffic Anomaly Detection Deep Learning And Statistical Fusion Feature Extraction
目 录
1绪论 1
1.1网络流量特征提取与异常检测的背景意义 1
1.2国内外研究现状综述 1
1.3本文研究方法与技术路线 2
2网络流量特征提取技术 2
2.1流量数据预处理方法 2
2.2特征选择与降维算法 3
2.3特征提取模型构建 3
3异常检测算法研究 4
3.1常见异常检测算法分析 4
3.2基于机器学习的检测方法 5
3.3混合检测算法设计 5
4实验验证与结果分析 6
4.1实验环境与数据集构建 6
4.2检测性能评估指标 7
4.3结果分析与讨论 7
结论 8
参考文献 9
致谢 10
随着互联网的迅猛发展，网络流量呈现出复杂多变的特征，异常流量对网络安全构成严重威胁。为此，本研究聚焦于网络流量特征提取与异常检测技术，旨在构建一种高效、精准的异常检测系统。通过深入分析现有方法存在的问题，提出基于深度学习与传统统计方法相结合的混合模型，利用深度神经网络强大的特征学习能力自动提取流量特征，并结合统计方法对特定场景下的流量进行精细化建模。实验结果表明，该方法在准确率和召回率方面均优于传统单一方法，在多种真实网络环境中实现了98%以上的检测精度。此外，针对大规模数据处理需求，设计了分布式计算框架以提高算法效率。
关键词：网络流量异常检测 深度学习与统计融合 特征提取
Abstract
With the rapid development of the Internet, the network traffic presents the complex and changeable characteristics, and the abnormal traffic poses a serious threat to the network security. To this end, this study focuses on the network traffic feature extraction and anomaly detection technology, aiming to build an efficient and accurate anomaly detection system. Through the in-depth analysis of the problems of the existing methods, the hybrid model based on deep learning and traditional statistical methods is proposed, using the powerful feature learning ability of deep neural network to automatically extract the traffic features, and fine model the traffic in a specific scene in combination with the statistical method. Experimental results show that the proposed method outperforms the traditional single method in both accuracy and recall, and achieves detection accuracy above 98% in multiple real network environments. In addition, a distributed computing fr amework is designed to improve the efficiency of the algorithms for large-scale data processing requirements.
Keyword:Network Traffic Anomaly Detection Deep Learning And Statistical Fusion Feature Extraction
目 录
1绪论 1
1.1网络流量特征提取与异常检测的背景意义 1
1.2国内外研究现状综述 1
1.3本文研究方法与技术路线 2
2网络流量特征提取技术 2
2.1流量数据预处理方法 2
2.2特征选择与降维算法 3
2.3特征提取模型构建 3
3异常检测算法研究 4
3.1常见异常检测算法分析 4
3.2基于机器学习的检测方法 5
3.3混合检测算法设计 5
4实验验证与结果分析 6
4.1实验环境与数据集构建 6
4.2检测性能评估指标 7
4.3结果分析与讨论 7
结论 8
参考文献 9
致谢 10
