基于Matlab的广义神经网络的聚类算法-网络入侵聚类

%% 清空环境文件 clear all; clc; %% 提取攻击数据 %攻击样本数据 load netattack; P1=netattack; T1=P1(:,39)'; P1(:,39)=[]; %数据大小 [R1,C1]=size(P1); csum=20; %提取训练数据多少 %% 模糊聚类 data=P1; [center,U,obj_fcn] = fcm(data,5); for i=1:R1 [value,idx]=max(U(:,i)); a1(i)=idx; end %% 模糊聚类结果分析 Confusion_Matrix_FCM=zeros(6,6); Confusion_Matrix_FCM(1,:)=[0:5]; Confusion_Matrix_FCM(:,1)=[0:5]'; for nf=1:5 for nc=1:5 Confusion_Matrix_FCM(nf+1,nc+1)=length(find(a1(find(T1==nf))==nc)); end end %% 网络训练样本提取 cent1=P1(find(a1==1),:);cent1=mean(cent1); cent2=P1(find(a1==2),:);cent2=mean(cent2); cent3=P1(find(a1==3),:);cent3=mean(cent3); cent4=P1(find(a1==4),:);cent4=mean(cent4); cent5=P1(find(a1==5),:);cent5=mean(cent5); %提取范数最小为训练样本 for n=1:R1; ecent1(n)=norm(P1(n,:)-cent1); ecent2(n)=norm(P1(n,:)-cent2); ecent3(n)=norm(P1(n,:)-cent3); ecent4(n)=norm(P1(n,:)-cent4); ecent5(n)=norm(P1(n,:)-cent5); end for n=1:csum [va me1]=min(ecent1); [va me2]=min(ecent2); [va me3]=min(ecent3); [va me4]=min(ecent4); [va me5]=min(ecent5); ecnt1(n,:)=P1(me1(1),:);ecent1(me1(1))=[];tcl(n)=1; ecnt2(n,:)=P1(me2(1),:);ecent2(me2(1))=[];tc2(n)=2; ecnt3(n,:)=P1(me3(1),:);ecent3(me3(1))=[];tc3(n)=3; ecnt4(n,:)=P1(me4(1),:);ecent4(me4(1))=[];tc4(n)=4; ecnt5(n,:)=P1(me5(1),:);ecent5(me5(1))=[];tc5(n)=5; end P2=[ecnt1;ecnt2;ecnt3;ecnt4;ecnt5];T2=[tcl,tc2,tc3,tc4,tc5]; k=0; %% 迭代计算 for nit=1:10%开始迭代 %% 广义神经网络聚类 net = newgrnn(P2',T2,50); %训练广义网络 a2=sim(net,P1') ; %预测结果 %输出标准化（根据输出来分类） a2(find(a2<=1.5))=1; a2(find(a2>1.5&a2<=2.5))=2; a2(find(a2>2.5&a2<=3.5))=3; a2(find(a2>3.5&a2<=4.5))=4; a2(find(a2>4.5))=5; %% 网络训练数据再次提取 cent1=P1(find(a2==1),:);cent1=mean(cent1); cent2=P1(find(a2==2),:);cent2=mean(cent2); cent3=P1(find(a2==3),:);cent3=mean(cent3); cent4=P1(find(a2==4),:);cent4=mean(cent4); cent5=P1(find(a2==5),:);cent5=mean(cent5); for n=1:R1%计算样本到各个中心的距离 ecent1(n)=norm(P1(n,:)-cent1); ecent2(n)=norm(P1(n,:)-cent2); ecent3(n)=norm(P1(n,:)-cent3); ecent4(n)=norm(P1(n,:)-cent4); ecent5(n)=norm(P1(n,:)-cent5); end %选择离每类中心最近的csum个样本 for n=1:csum [va me1]=min(ecent1); [va me2]=min(ecent2); [va me3]=min(ecent3); [va me4]=min(ecent4); [va me5]=min(ecent5); ecnt1(n,:)=P1(me1(1),:);ecent1(me1(1))=[];tc1(n)=1; ecnt2(n,:)=P1(me2(1),:);ecent2(me2(1))=[];tc2(n)=2; ecnt3(n,:)=P1(me3(1),:);ecent3(me3(1))=[];tc3(n)=3; ecnt4(n,:)=P1(me4(1),:);ecent4(me4(1))=[];tc4(n)=4; ecnt5(n,:)=P1(me5(1),:);ecent5(me5(1))=[];tc5(n)=5; end p2=[ecnt1;ecnt2;ecnt3;ecnt4;ecnt5];T2=[tc1,tc2,tc3,tc4,tc5]; %统计分类结果 Confusion_Matrix_GRNN=zeros(6,6); Confusion_Matrix_GRNN(1,:)=[0:5]; Confusion_Matrix_GRNN(:,1)=[0:5]'; for nf=1:5 for nc=1:5 Confusion_Matrix_GRNN(nf+1,nc+1)=length(find(a2(find(T1==nf))==nc)); end end pre2=0; for n=2:6; pre2=pre2+max(Confusion_Matrix_GRNN(n,:)); end pre2=pre2/R1*100; end %% 结果显示 Confusion_Matrix_FCM Confusion_Matrix_GRNN