摘 要
随着互联网技术的迅猛发展，网络流量呈现出爆炸式增长态势，准确预测网络流量并及时检测异常对于保障网络安全稳定运行至关重要。本研究旨在基于深度学习构建网络流量预测与异常检测模型，以提高预测精度和检测效率。首先收集了某大型网络运营商提供的包含正常及异常流量样本的数据集，对数据进行预处理包括缺失值填补、标准化等操作。采用长短期记忆网络（LSTM）作为核心算法，因其能有效捕捉时间序列中的长期依赖关系，在模型构建方面，将网络流量数据按时间顺序输入LSTM网络，通过多层神经元的学习训练，使模型具备强大的特征提取能力。为增强模型鲁棒性，引入注意力机制，让模型聚焦于关键流量特征，这是本研究的一大创新点。
关键词：网络流量预测 异常检测 长短期记忆网络
Abstract
With the rapid development of Internet technology, network traffic shows an explosive growth trend. Accurate prediction of network traffic and timely detection of abnormalities is very important to ensure the security and stable operation of network. This study aims to build network traffic prediction and anomaly detection models based on deep learning to improve prediction accuracy and detection efficiency. First, the data set provided by a large network operator was collected, and the data was preprocessed including missing value filling, standardization and other operations. Long-and short-term memory network (LSTM) is adopted as the core algorithm, which can effectively capture the long-term dependencies in the time series. In terms of model construction, the network traffic data is input into LSTM network in chronological order, and through the learning and training, the model has powerful feature extraction ability. To enhance the robustness of the model, the attention mechanism is introduced to make the model focus on the key flow characteristics, which is a major innovation of this study.
Keyword:Network Traffic Prediction Anomaly Detection Long Short-Term Memory Network
目 录
1绪论 1
1.1研究背景与意义 1
1.2国内外研究现状 1
1.3本文研究方法 1
2深度学习模型选择与构建 2
2.1流量预测模型架构 2
2.2异常检测算法设计 3
2.3模型训练与优化策略 3
3数据预处理与特征工程 4
3.1原始数据获取与清洗 4
3.2特征提取与选择 4
3.3数据集构建与划分 5
4实验结果分析与评估 6
4.1预测性能评估指标 6
4.2异常检测效果分析 6
4.3结果讨论与改进方向 7
结论 8
参考文献 9
致谢 10
随着互联网技术的迅猛发展，网络流量呈现出爆炸式增长态势，准确预测网络流量并及时检测异常对于保障网络安全稳定运行至关重要。本研究旨在基于深度学习构建网络流量预测与异常检测模型，以提高预测精度和检测效率。首先收集了某大型网络运营商提供的包含正常及异常流量样本的数据集，对数据进行预处理包括缺失值填补、标准化等操作。采用长短期记忆网络（LSTM）作为核心算法，因其能有效捕捉时间序列中的长期依赖关系，在模型构建方面，将网络流量数据按时间顺序输入LSTM网络，通过多层神经元的学习训练，使模型具备强大的特征提取能力。为增强模型鲁棒性，引入注意力机制，让模型聚焦于关键流量特征，这是本研究的一大创新点。
关键词：网络流量预测 异常检测 长短期记忆网络
Abstract
With the rapid development of Internet technology, network traffic shows an explosive growth trend. Accurate prediction of network traffic and timely detection of abnormalities is very important to ensure the security and stable operation of network. This study aims to build network traffic prediction and anomaly detection models based on deep learning to improve prediction accuracy and detection efficiency. First, the data set provided by a large network operator was collected, and the data was preprocessed including missing value filling, standardization and other operations. Long-and short-term memory network (LSTM) is adopted as the core algorithm, which can effectively capture the long-term dependencies in the time series. In terms of model construction, the network traffic data is input into LSTM network in chronological order, and through the learning and training, the model has powerful feature extraction ability. To enhance the robustness of the model, the attention mechanism is introduced to make the model focus on the key flow characteristics, which is a major innovation of this study.
Keyword:Network Traffic Prediction Anomaly Detection Long Short-Term Memory Network
目 录
1绪论 1
1.1研究背景与意义 1
1.2国内外研究现状 1
1.3本文研究方法 1
2深度学习模型选择与构建 2
2.1流量预测模型架构 2
2.2异常检测算法设计 3
2.3模型训练与优化策略 3
3数据预处理与特征工程 4
3.1原始数据获取与清洗 4
3.2特征提取与选择 4
3.3数据集构建与划分 5
4实验结果分析与评估 6
4.1预测性能评估指标 6
4.2异常检测效果分析 6
4.3结果讨论与改进方向 7
结论 8
参考文献 9
致谢 10
