PSO优化VIC算法WORM蠕虫检测Matlab仿真实践

一、理论基础

根据参考文献：

基于PSO优化VIC算法的WORM蠕虫检测matlab仿真_建模

初始条件

基于PSO优化VIC算法的WORM蠕虫检测matlab仿真_粒子群_02

计算曲率

基于PSO优化VIC算法的WORM蠕虫检测matlab仿真_WORM蠕虫检测_03

角度的计算公式

基于PSO优化VIC算法的WORM蠕虫检测matlab仿真_WORM蠕虫检测_04

基于PSO优化VIC算法的WORM蠕虫检测matlab仿真_WORM蠕虫检测_05

基于PSO优化VIC算法的WORM蠕虫检测matlab仿真_VIC算法_06

那么对于坐标点，其满足如下的计算公式，

基于PSO优化VIC算法的WORM蠕虫检测matlab仿真_建模_07

二、案例背景

1.问题描述

给定一组初始值，构造出虚拟图。初始值就是如下图图一 x0, y0, θ(0)那行，给出曲线的初始点和初始角度，构造出曲线。曲线中的current point x的坐标可以由θ（s）求出，θ（s）可以由初始值求出。如下图一所示 K（s）是一条曲线的曲率，用傅里叶级数表达然后求出角θ（s），这样，这条曲线就被描绘出来了，然后用这条曲线构造一个virtual object，如图二所示。这个object 是曲线以半径r外扩得到的长条形曲面，如图所示。

基于PSO优化VIC算法的WORM蠕虫检测matlab仿真_VIC算法_08

2.思路流程

本课题我们主要通过VIC算法检测得到曲线的中线，然后根据这个中线进行扩展，得到光滑曲线体，本课题的这个研究过程和实际的蠕虫建模方法非常的接近，这是由于光滑曲线体和蠕虫的建模，他们都是通过中线检测进行的。此外，本课题还对传统的VIC算法进行了改进，通过使用PSO粒子群优化算法，从而大大提供的光滑曲线的建模精度。

通过PSO粒子群优化算法，对原有的VIC算法进行参数进行优化，从而得到更高精度的虚拟曲线的建模。通过仿真对比可知，采用优化算法之后的虚拟曲线，其精度比原算法的精度提高了10倍以上。

三、部分MATLAB程序

VIC算法部分：

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function I2 = func_Virtual_Image(X,Y,X_img,Y_img,L,Ls,r,theta);%然后用这条曲线构造一个virtual object，如图二所示。这个object 是曲线以半径r外扩得到的长条形曲面，如图所示。%进行扩展I2 = 50*ones(L,L); for kk = 1:r X1 = zeros(L/Ls,1); Y1 = zeros(L/Ls,1); X2 = zeros(L/Ls,1); Y2 = zeros(L/Ls,1); for i = 1:L/Ls if mod(sum(theta(1:i)),180) < 0 X1(i) = X(i) + kk*cos(pi*theta(i)/180); Y1(i) = Y(i) - kk*sin(pi*theta(i)/180); X2(i) = X(i) - kk*cos(pi*theta(i)/180); Y2(i) = Y(i) + kk*sin(pi*theta(i)/180); else X1(i) = X(i) + kk*cos(pi/2 - pi*theta(i)/180); Y1(i) = Y(i) - kk*sin(pi/2 - pi*theta(i)/180); X2(i) = X(i) - kk*cos(pi/2 - pi*theta(i)/180); Y2(i) = Y(i) + kk*sin(pi/2 - pi*theta(i)/180); end end %将X和Y曲线变化为实际图像信息 for i = 1:L/Ls Xp1(i) = floor(X1(i)); Yp1(i) = floor(Y1(i)); Xp2(i) = floor(X2(i)); Yp2(i) = floor(Y2(i)); end X_img1 = L-Yp1; Y_img1 = Xp1; X_img2 = L-Yp2; Y_img2 = Xp2; for i = 1:L/Ls I2(X_img(i),Y_img(i)) = 255; if r==1 I2(X_img1(i),Y_img1(i)) = 255 - kk*120; I2(X_img2(i),Y_img2(i)) = 255 - kk*120; end if r==2 I2(X_img1(i),Y_img1(i)) = 255 - kk*90; I2(X_img2(i),Y_img2(i)) = 255 - kk*90; end if r==3 I2(X_img1(i),Y_img1(i)) = 255 - kk*80; I2(X_img2(i),Y_img2(i)) = 255 - kk*80; end if r==4 I2(X_img1(i),Y_img1(i)) = 255 - kk*60; I2(X_img2(i),Y_img2(i)) = 255 - kk*60; end if r==5 I2(X_img1(i),Y_img1(i)) = 255 - kk*50; I2(X_img2(i),Y_img2(i)) = 255 - kk*50; end if r==6 I2(X_img1(i),Y_img1(i)) = 255 - kk*40; I2(X_img2(i),Y_img2(i)) = 255 - kk*40; end if r==7 I2(X_img1(i),Y_img1(i)) = 255 - kk*35; I2(X_img2(i),Y_img2(i)) = 255 - kk*35; end if r==8 I2(X_img1(i),Y_img1(i)) = 255 - kk*30; I2(X_img2(i),Y_img2(i)) = 255 - kk*30; end if r==9 I2(X_img1(i),Y_img1(i)) = 255 - kk*25; I2(X_img2(i),Y_img2(i)) = 255 - kk*25; end if r==10 I2(X_img1(i),Y_img1(i)) = 255 - kk*25; I2(X_img2(i),Y_img2(i)) = 255 - kk*25; end if r==11 I2(X_img1(i),Y_img1(i)) = 255 - kk*23; I2(X_img2(i),Y_img2(i)) = 255 - kk*23; end if r==12 I2(X_img1(i),Y_img1(i)) = 255 - kk*21; I2(X_img2(i),Y_img2(i)) = 255 - kk*21; end if r==13 I2(X_img1(i),Y_img1(i)) = 255 - kk*19; I2(X_img2(i),Y_img2(i)) = 255 - kk*19; end if r==14 I2(X_img1(i),Y_img1(i)) = 255 - kk*18; I2(X_img2(i),Y_img2(i)) = 255 - kk*18; end if r==15 I2(X_img1(i),Y_img1(i)) = 255 - kk*17; I2(X_img2(i),Y_img2(i)) = 255 - kk*17; end if r==16 I2(X_img1(i),Y_img1(i)) = 255 - kk*16; I2(X_img2(i),Y_img2(i)) = 255 - kk*16; end end endI2 = medfilt2(I2,[5,5]);1.2.3.4.5.6.7.8.9.10.11.12.13.14.15.16.17.18.19.20.21.22.23.24.25.26.27.28.29.30.31.32.33.34.35.36.37.38.39.40.41.42.43.44.45.46.47.48.49.50.51.52.53.54.55.56.57.58.59.60.61.62.63.64.65.66.67.68.69.70.71.72.73.74.75.76.77.78.79.80.81.82.83.84.85.86.87.88.89.90.91.92.93.94.95.96.97.98.99.100.101.102.103.104.105.106.107.108.109.110.111.112.113.114.115.116.117.118.

PSO优化：

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while iter<iter_max iter=iter+1; for i=1:N alpha(1) = X(1,i); alpha(2) = X(2,i); alpha(3) = X(3,i); V_score = func_cal_score(alpha,L,m,Theta,Ls,Xt,Yt,r,I2); J=1/(1+(V_score)); if J>fpbest(i) fpbest(i) = J; Xpbest(:,i) = X(:,i); end end [fitnessmax,index]=max(fpbest); if fitnessmax>fgbest fgbest=fitnessmax; Xgbest=X(:,index); end for i=1:N r1 = rand; r2 = rand; fai1 = C1*r1; fai2 = C2*r2; V(:,i) = w(iter) * V(:,i) +fai1 *( Xpbest(:,i) - X(:,i) ) +fai2 * ( Xgbest(:,1) - X(:,i) ); index = find(abs(V(:,i))>Vmax); if(any(index)) V(index,i) = V(index,i)./abs(V(index,i)).*Vmax; end X(:,i) = X(:,i)+V(:,i); end fgbest_fig(iter) = fgbest; Xgbest_fig(:,iter) = Xgbest; alpha(1) = Xgbest_fig(1,iter); alpha(2) = Xgbest_fig(2,iter); alpha(3) = Xgbest_fig(3,iter); V_scores = func_cal_score(alpha,L,m,Theta,Ls,Xt,Yt,r,I2); V_score2(iter) = V_scores; end1.2.3.4.5.6.7.8.9.10.11.12.13.14.15.16.17.18.19.20.21.22.23.24.25.26.27.28.29.30.31.32.33.34.35.36.37.38.39.

VIC算法的主程序调用：

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............................%然后用这条曲线构造一个virtual object，如图二所示。这个object 是曲线以半径r外扩得到的长条形曲面，如图所示。%进行扩展X1 = zeros(L/Ls,1);Y1 = zeros(L/Ls,1);X2 = zeros(L/Ls,1);Y2 = zeros(L/Ls,1);for i = 1:L/Ls if mod(sum(theta(1:i)),180) < 0 X1(i) = X(i) + r*cos(pi*theta(i)/180); Y1(i) = Y(i) - r*sin(pi*theta(i)/180); X2(i) = X(i) - r*cos(pi*theta(i)/180); Y2(i) = Y(i) + r*sin(pi*theta(i)/180); else X1(i) = X(i) + r*cos(pi/2 - pi*theta(i)/180); Y1(i) = Y(i) - r*sin(pi/2 - pi*theta(i)/180); X2(i) = X(i) - r*cos(pi/2 - pi*theta(i)/180); Y2(i) = Y(i) + r*sin(pi/2 - pi*theta(i)/180); endendif Ls == 1 figure; subplot(121) plot(X,Y,'b');hold on; plot(X,Y,'k.');hold on; plot(X1,Y1,'r');hold on; plot(X2,Y2,'r');hold on; for i = 1:length(X) line([X2(i),X1(i)],[Y2(i),Y1(i)],'Color',[1 0 1]);hold on; end title('virtual object'); grid on axis square axis([0,L,0,L]);else figure; plot(X,Y,'b');hold on; plot(X,Y,'k.');hold on; plot(X1,Y1,'r');hold on; plot(X2,Y2,'r');hold on; for i = 1:length(X) line([X2(i),X1(i)],[Y2(i),Y1(i)],'Color',[1 0 1]);hold on; end title('virtual object'); grid on axis square axis([0,L,0,L]); end%产生虚拟图像%将X和Y曲线变化为实际图像信息I2 = func_Virtual_Image(X,Y,X_img,Y_img,L,Ls,r,theta);if Ls == 1 subplot(122) imshow(I2,[]) title('被描绘出来的曲线'); axis squareend 1.2.3.4.5.6.7.8.9.10.11.12.13.14.15.16.17.18.19.20.21.22.23.24.25.26.27.28.29.30.31.32.33.34.35.36.37.38.39.40.41.42.43.44.45.46.47.48.49.50.51.52.53.54.55.56.57.58.59.60.61.62.63.64.65.66.