摘要
针对网络入侵检测系统中检测率低的问题,本文提出一种基于双隶属度FSVM的非平衡数据分类方法。该方法考虑到训练样本存在样本不平衡和噪声污染样本问题,首先利用FCM聚类方法计算类内平衡隶属度,构成模糊隶属函数,同时考虑到样本间数量、分散度等因素导致样本间不平衡的问题,在模糊隶属函数中引入类间平衡隶属度,然后设计一种基于双隶属度FSVM对不平衡样本进行机器学习和分类。
Aiming at the low detection rate in network intrusion detection system,we present a method of imbalanced data classification based on Double Factor FSVM(DM⁃FSVM).Considering the problem of imbalance and noise and isolated points in the training sample,the FCM clustering method is used to calculate the intra⁃class imbalanced factor to form the fuzzy membership function.The sample imbalance is caused by factors such as the number and the dispersion of samples.Therefore,inter⁃class imbalanced factors were introduced in the fuzzy membership function.And machine learning and classification were designed for imbalanced samples based on DM⁃FSVM.
作者
朱玺
温志强
ZHU Xi;WEN Zhiqiang(State Grid Shanghai Municipal Electric Power Company Information Communication Company,Shanghai 200240,China)
出处
《电子设计工程》
2020年第22期52-55,60,共5页
Electronic Design Engineering
