基于Fisher_BP的入侵检测方法研究  

INTRUSION DETECTION METHOD BASED ON FISHER_BP

作　　者：万佳蓉 王绍杰 Wan Jiarong;Wang Shaojie(National Computer System Engineering Research Institute of China,Beijing 100083,China)

机构地区：[1]华北计算机系统工程研究所,北京100083

出　　处：《计算机应用与软件》2025年第3期371-376,共6页Computer Applications and Software

摘　　要：工业控制系统面临的威胁增多,为做到主动防御,以及更加准确地识别入侵数据类型,基于工控蜜罐的部署环境,设计一种模型,用来识别入侵数据具体类型。首先对捕获的数据进行核主成分分析(Kernel Principal Component Analysis,KPCA)降维,然后利用Fisher算法对处理后的数据进行分类,如果判定为异常类,则再利用BP神经网络(Back Propagation neural network)进行二次判别,确定具体的入侵类别。实验结果表明,该方法检测率可达到95%,可以较好地对数据进行分类,判定具体的入侵类型。Industrial control systems face increasing threats.In order to achieve active defense and more accurate identification of intrusion data types,based on the deployment environment of industrial control honeypots,a model is designed to identify specific types of intrusion data.This model performed kernel principal component analysis(KPCA)dimensionality reduction on the captured data,and used the Fisher algorithm to classify the processed data.If it was determined to be an abnormal class,the BP neural network was used for secondary discrimination to determine the specific type of intrusion.The experimental results show that the detection rate of this method can reach 95%,and it can classify the data well and determine the specific type of intrusion.

关 键 词：工业控制系统 工控蜜罐 核主成分分析 FISHER判别 BP神经网络 

分 类 号：TP399[自动化与计算机技术—计算机应用技术]