基于ReliefF算法的通信网络蠕虫病毒高效检测方法
Efficient Detection Method for Communication Network Worm Virus Based on ReliefF
庞姚1
作者信息
摘要
由于传统方法对通信网络蠕虫病毒的检测效果不佳,提出基于ReliefF算法的通信网络蠕虫病毒高效检测方法.首先采用ReliefF算法采集通信网络蠕虫病毒特征,并根据应用程序接口(Application Programming Interface,API)的调用筛选特征;其次将筛选出来的蠕虫病毒特征作为训练样本,将经过特征选择后的训练样本输入分类器中进行模型构建;最后通过交叉验证方法优化模型,并使用构建的模型检测文件中是否含有蠕虫病毒.实验结果表明,采用基于ReliefF算法的通信网络蠕虫病毒检测方法准确率较高,且优于其他两种方法.
Abstract
Due to the poor detection performance of traditional methods,an efficient detection method for communication network worm viruses based on ReliefF algorithm is proposed.Firstly,the ReliefF algorithm is used to collect the characteristics of communication network worm viruses,and the features are filtered based on Application Programming Interface(API)calls.Secondly,the selected worm virus features are used as training samples,and the selected training samples are input into the classifier for model construction.Finally,the model was optimized using cross validation methods and the constructed model was used to detect the presence of worm viruses in the file.The experimental results show that the communication network worm virus detection method based on ReliefF algorithm has a high accuracy and is superior to the other two methods.
关键词
数据挖掘/通信网络/蠕虫病毒/检测方法Key words
data mining/communication network/worm virus/detection method引用本文复制引用
出版年
2024
NETL
NSTL
万方数据