基于机器学习的网络流量分析与通信性能优化  

Network Traffic Analysis and Communication Performance Optimization Based on Machine Learning

作　　者：王陈希 WANG Chenxi(Information Operation and Maintenance Room of Comprehensive Information Support Center of the First Mobile Armed Police Force,Shijiazhuang 050800,China)

机构地区：[1]武警第一机动总队综合信息保障中心信息运维室,河北石家庄050800

出　　处：《通信电源技术》2024年第13期142-144,共3页Telecom Power Technology

摘　　要：文章提出一种基于机器学习的网络流量分析与通信性能优化方法,旨在提升网络管理的安全性和效率。通过设计有效的特征提取方法,将网络流量数据转化为可处理的特征矩阵,并进行数据预处理和标签设置,以构建具有代表性和均衡性的数据集。采用卷积神经网络模型进行协议识别,并通过模型训练和参数调优技术优化通信性能。该方法在网络流量分析中具有有效性和应用潜力,为提升网络安全防御能力和通信性能提供了新的思路和方法。This paper proposes a method of network traffic analysis and communication performance optimization based on machine learning,aiming at improving the security and efficiency of network management.By designing an effective feature extraction method,the network traffic data is transformed into a processable feature matrix,and data preprocessing and label setting are carried out to construct a representative and balanced data set.Convolutional neural network model is used for protocol identification,and communication performance is optimized through model training and parameter tuning technology.This method has effectiveness and application potential in network traffic analysis,and provides new ideas and methods for improving network security defense capability and communication performance.

关 键 词：机器学习 网络流量分析 协议识别 特征提取 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]