I want to use this Entropy based recommendation algorithm in fishing out some malicious nodes in internet of thing security analysis but I am having a little challenges about what the initials I highlighted represent. For instance tp represent "true positive", just like fp represent "false positive" Please I need help
tp=zeros(10,10); fp=zeros(10,10); phh=zeros(10,10); pmm=zeros(10,10); pmh=zeros(10,10); phm=zeros(10,10); M=zeros(10,10); H=zeros(10,10); source=zeros(10,200,100); avgT=zeros(10,10); eval= zeros(1, 100); sim = zeros(10, 1); se=zeros(10,1); ce =zeros(10,1); pc= zeros(10,100); c=zeros(10,100); T=zeros(100,100,10); actM=[1 1 1 1 1 0 0 0 0 0]; %cw2= [ 1 0 0 0 0 1 1 1 1 1] ; %tq=zeros(10,T); cw3=zeros(1,100); q=10; while q < 40 d=1; T=zeros(10,100,100); while d <5 %%%%%%%%%%%%%%%%%%% iterating over percentage of malicious packets
phh=zeros(10,10); pmm=zeros(10,10); pmh=zeros(10,10); phm=zeros(10,10); M=zeros(10,10); H=zeros(10,10); iter=1; while iter < 20 %%%%%% look into the following code, it does not run for iterations %%%%%%%%%%%%%%%% generating multiple runs for the given percentage %%%%%%%%%%%%%%%% of malicious packets
eval=zeros(1,100);
rp=randperm(100);
eval(rp(1:q)) = 1;
cw1=bsc(eval,0.04);
cw2=zeros(1,100);
cw2=bsc(eval,(q/100));
source=zeros(100,200,100);
sim = zeros(10, 1); se=zeros(10,1); ce =zeros(10,1);
pc= zeros(10,100);
c=zeros(10,100);
for i=1:100
for j=1:10
source(j,i,:) =eval;
if rem(i,10)<(d )&& j<((10/2) +1) &&i>1
source(j,i,:)=cw2;
end
for k=1:100
if (i ==1)
c(j,k)=1;
end
if (i>1&& source(j,i,k) == source(j,i-1,k))
c(j,k) =c(j,k)+1;
end
end
sim(j)=similar(source(j,i,:),eval);
se(j)= -sim(j)*log10(sim(j))/log10(100);
if isnan(se(j))
se(j)=1;
end
pc(j,:)=c(j,:)/i; pc(1,:)=c(1,:)/i;
sum =0;
for k=1:100
sum=(-pc(j,k)*log10(pc(j,k))/log10(100) )+ sum;
end
ce(j) =sum;
if isnan(ce(j))
ce(j)=1;
end
if i <2
T(d,i,j)=1-se(j);
else
T(d,i,j)=(1-(se(j)+ce(j)))*0.5+ 0.5*T(d,i-1,j);
T(d,i,j)= T(d,i,j)/max(T(d,:,j));
end
if (isnan(T(d,i,j)) )
T(d,i,j)=0;
end
if (T(d,i,j)>1)
T(d,i,j)=1;
end
if (T(d,i,j)<0)
T(d,i,j)=0;
end
end
end
for i = 1: 10 % d*10 to 10 avgT(d,i)=mean(T(d,:,i)); %%%%%%%%%% replacing 'd 'by d if avgT(d,i)<0.75 M(d,i)=M(d,i)+1; if actM(i)==1 pmm (q/10,d)= pmm(q/10,d)+1; else pmh(q/10,d) =pmh(q/10,d)+1; end else H(d,i)=H(d,i)+1; if actM(i)==0 phh(q/10,d)=phh(q/10,d)+1; else phm(q/10,d) =phm(q/10,d)+1; end end end iter =iter+1; end % tp(q/10,d)=pmm(q/10,d)/(pmm(q/10,d)+phm(q/10,d)); tp(q/10,d)=pmm(q/10,d)/(pmm(q/10,d)+phm(q/10,d)); % fp(q/10,d)=pmh(q/10,d)/(phh(q/10,d)+pmh(q/10,d)); fp(q/10,d)=pmh(q/10,d)/(phh(q/10,d)+pmh(q/10,d)); d=d+1;
end
q =q+10; x=1:100; figure(q/10) plot(x,T(1,:,9),'-ob', x,T(1,:,2),'-dr', x, T(2,:,2),'-sg',x,T(3,:,2),'-m*',x,T(5,:,2),'-k^'); legend('Honest', 'Malicious, p =0.1, 10% recommendations are false', 'Malicious,p=0.2, 10% recommendations are false','Malicious,p=0.3, 10% recommendations are false','Malicious,p=0.5, 10% recommendations are false') xlabel('Iteration'); ylabel('RecommendationTrust'); title('Impact of size of recommendation vector');
end
