基于数据挖掘技术的计算机网络病毒防御方法研究  

Research on computer network virus defense methods based on data mining technology

作　　者：姜育生 JIANG Yusheng(Shaanxi Open University,Xi’an 710119,China)

机构地区：[1]陕西开放大学,陕西西安710119

出　　处：《电子设计工程》2025年第7期145-149,共5页Electronic Design Engineering

基　　金：陕西省教育厅科学研究计划项目资助(22JZ019)。

摘　　要：为了有效防御计算机网络病毒的入侵,保障网络信息安全,充分发挥数据挖掘技术在识别和分析网络病毒行为模式方面的作用,建立网络病毒检测模型,对网络病毒样本进行特征提取和模式识别。通过对网络流量数据进行挖掘并分析系统变化状态以及病毒的扩散趋势,得出病毒的传播路径、感染特征和攻击方式。设定聚类目标函数和聚类中心对数据样本类型进行划分,确定病毒疫区和易感节点并在接收端设置隔离墙以阻断病毒的传播扩散,从而实现对网络病毒的有效防御。实验结果表明,基于数据挖掘技术的网络病毒防御方法可以准确检测网络病毒类型并有效对其进行阻断防御,切实提高了计算机网络系统的病毒防御能力。In order to effectively defend against the invasion of computer network viruses,ensure network information security,and fully leverage the effective role of data mining technology in identifying and analyzing network virus behavior patterns.By establishing a network virus detection model,feature extraction and pattern recognition are performed on network virus samples;By mining network traffic data and analyzing system changes and the spread trend of viruses,we can determine the transmission path,infection characteristics,and attack methods of viruses.By setting clustering objective functions and clustering centers to partition data sample types,virus epidemic areas and susceptible nodes are determined,and isolation walls are set up at the receiving end to block the spread of viruses,thereby achieving effective defense against network viruses.The experimental results show that the network virus defense method based on data mining technology can accurately detect network virus types and effectively block them,effectively improving the virus defense ability of computer network systems.

关 键 词：计算机网络 病毒防御 检测模型 数据挖掘技术 模糊均值聚类 隔离墙 

分 类 号：TN929.5[电子电信—通信与信息系统]