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。本研究旨在构建基于深度学习的网络流量预测与异常检测模型，以提高预测精度和异常检测效率。采用长短期记忆网络（LSTM）作为核心算法，针对网络流量时间序列数据非线性、非平稳特性进行建模，同时引入注意力机制增强模型对关键特征的学习能力。通过收集某大型数据中心一年内的实际流量数据集进行实验验证，将所提方法与传统ARIMA模型、BP神经网络等进行对比分析。结果表明，该模型在预测准确性方面有显著提升，均方误差降低约30%，且能够更早地发现异常流量波动，平均提前预警时间为15分钟。此外，创新性地提出一种自适应阈值设定方法用于异常判定，有效解决了固定阈值法存在的误报率高问题。本研究为网络流量管理提供了新的思路和技术手段，在智能运维、安全防护等领域具有广阔应用前景。
关键词：网络流量预测；深度学习；异常检测
Abstract
With the rapid development of Internet technology, network traffic has exhibited an explosive growth trend. Accurate prediction of network traffic and timely detection of anomalies are crucial for ensuring the secure and stable operation of networks. This study aims to construct a deep learning-based model for network traffic prediction and anomaly detection to improve prediction accuracy and anomaly detection efficiency. Long Short-Term Memory (LSTM) networks are employed as the core algorithm to model the nonlinear and non-stationary characteristics of network traffic time series data, while an attention mechanism is introduced to enhance the model's ability to learn key features. The proposed method was validated using an actual traffic dataset collected from a large data center over one year, and comparative analysis was conducted with traditional ARIMA models and BP neural networks. Results indicate that the model significantly improves prediction accuracy, reducing mean squared error by approximately 30%, and can detect abnormal traffic fluctuations earlier, with an average early warning time of 15 minutes. Additionally, an innovative adaptive threshold setting method for anomaly determination is proposed, effectively addressing the high false alarm rate associated with fixed threshold methods. This research provides new insights and technical approaches for network traffic management, offering broad application prospects in areas such as intelligent operations and security protection.
Keywords：Network Traffic Prediction; Deep Learning; Anomaly Detection
目 录
摘要 I
Abstract II
一、绪论 1
(一) 研究背景与意义 1
(二) 国内外研究现状 1
(三) 本文研究方法 2
二、深度学习模型选择与构建 2
(一) 流量预测模型架构设计 2
(二) 异常检测算法选取 3
(三) 模型训练与优化策略 4
三、数据预处理与特征提取 4
(一) 原始流量数据获取 4
(二) 数据清洗与预处理 5
(三) 特征选择与提取方法 6
四、实验结果与分析 7
(一) 实验环境与数据集 7
(二) 预测性能评估指标 7
(三) 异常检测效果分析 8
结 论 10
参考文献 11
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。本研究旨在构建基于深度学习的网络流量预测与异常检测模型，以提高预测精度和异常检测效率。采用长短期记忆网络（LSTM）作为核心算法，针对网络流量时间序列数据非线性、非平稳特性进行建模，同时引入注意力机制增强模型对关键特征的学习能力。通过收集某大型数据中心一年内的实际流量数据集进行实验验证，将所提方法与传统ARIMA模型、BP神经网络等进行对比分析。结果表明，该模型在预测准确性方面有显著提升，均方误差降低约30%，且能够更早地发现异常流量波动，平均提前预警时间为15分钟。此外，创新性地提出一种自适应阈值设定方法用于异常判定，有效解决了固定阈值法存在的误报率高问题。本研究为网络流量管理提供了新的思路和技术手段，在智能运维、安全防护等领域具有广阔应用前景。
关键词：网络流量预测；深度学习；异常检测
Abstract
With the rapid development of Internet technology, network traffic has exhibited an explosive growth trend. Accurate prediction of network traffic and timely detection of anomalies are crucial for ensuring the secure and stable operation of networks. This study aims to construct a deep learning-based model for network traffic prediction and anomaly detection to improve prediction accuracy and anomaly detection efficiency. Long Short-Term Memory (LSTM) networks are employed as the core algorithm to model the nonlinear and non-stationary characteristics of network traffic time series data, while an attention mechanism is introduced to enhance the model's ability to learn key features. The proposed method was validated using an actual traffic dataset collected from a large data center over one year, and comparative analysis was conducted with traditional ARIMA models and BP neural networks. Results indicate that the model significantly improves prediction accuracy, reducing mean squared error by approximately 30%, and can detect abnormal traffic fluctuations earlier, with an average early warning time of 15 minutes. Additionally, an innovative adaptive threshold setting method for anomaly determination is proposed, effectively addressing the high false alarm rate associated with fixed threshold methods. This research provides new insights and technical approaches for network traffic management, offering broad application prospects in areas such as intelligent operations and security protection.
Keywords：Network Traffic Prediction; Deep Learning; Anomaly Detection
目 录
摘要 I
Abstract II
一、绪论 1
(一) 研究背景与意义 1
(二) 国内外研究现状 1
(三) 本文研究方法 2
二、深度学习模型选择与构建 2
(一) 流量预测模型架构设计 2
(二) 异常检测算法选取 3
(三) 模型训练与优化策略 4
三、数据预处理与特征提取 4
(一) 原始流量数据获取 4
(二) 数据清洗与预处理 5
(三) 特征选择与提取方法 6
四、实验结果与分析 7
(一) 实验环境与数据集 7
(二) 预测性能评估指标 7
(三) 异常检测效果分析 8
结 论 10
参考文献 11
