//Takes in weights, phi challenges and target response bits, gives average error of APUF model
public override double ObjFunValue(double[] weightVector, sbyte[][] phiChallenges, sbyte[] targets)
{
double error = 0;
if (AppConstants.UseParallelismOnSingleCMAES == true)
{
int bitNum = phiChallenges[0].Length - 1;
double[] errorArray = new double[AppConstants.CoreNumber]; //Array for storing errors in parallel
XORArbiterPUF xModel = new XORArbiterPUF(bitNum, weightVector);
int sampleNumber = phiChallenges.Length; //Number of challenge-response pairs (number of training samples)
int blockSize = AppConstants.TrainingSize / AppConstants.CoreNumber;
Parallel.For(0, AppConstants.CoreNumber, coreIndex =>
//for (int coreIndex = 0; coreIndex < AppConstants.CoreNumber; coreIndex++)
{
for (int sampleIndex = coreIndex * blockSize; sampleIndex < (coreIndex + 1) * blockSize; sampleIndex++)
{
sbyte currentTarget = targets[sampleIndex];
sbyte modelOutput = xModel.ComputeResponse(phiChallenges[sampleIndex]);
if (modelOutput != currentTarget)
{
errorArray[coreIndex]++;
}
//Console.Out.WriteLine(sampleIndex.ToString());
}
});
//Time to sum the errors together
for (int i = 0; i < AppConstants.CoreNumber; i++)
{
error = error + errorArray[i];
}
error = error / (double)sampleNumber; //Give the average error
}
else if (AppConstants.UseParallelismOnSingleCMAES == false)
{
int bitNum = phiChallenges[0].Length - 1;
XORArbiterPUF xModel = new XORArbiterPUF(bitNum, weightVector);
int sampleNumber = phiChallenges.Length; //Number of challenge-response pairs (number of training samples)
for (int currentSample = 0; currentSample < sampleNumber; currentSample++)
{
sbyte currentTarget = targets[currentSample];
sbyte modelOutput = xModel.ComputeResponse(phiChallenges[currentSample]);
if (modelOutput != currentTarget)
{
error++;
}
}
error = error / (double)sampleNumber; //Give the average error
}
return(error);
}
public object Clone()
{
XORArbiterPUF xCopy = new XORArbiterPUF(NumPUF);
//Copy over the shallow variables (can improve this using shallow method copy later)
xCopy.BitNumber = BitNumber;
xCopy.MeanForAPUF = MeanForAPUF;
xCopy.VarianceForAPUF = VarianceForAPUF;
xCopy.NoiseMeanForAPUF = NoiseMeanForAPUF;
xCopy.NoiseVarianceForAPUF = NoiseVarianceForAPUF;
//Copy each individual APUF into the array
for (int i = 0; i < NumPUF; i++)
{
xCopy.ArbiterPUFArray[i] = (ArbiterPUF)ArbiterPUFArray[i].Clone();
}
return(xCopy);
}
//This attacks a single XOR APUF with parallelization done on a single run
//Note we DO NOT have recovery for this type of attack
private void xorAttackBtn_Click(object sender, EventArgs e)
{
string mainDirectory = "C:\\Users\\Windows\\Desktop\\Kaleel\\PUF Work\\Data64-4XOR";
string trainDir = mainDirectory + "\\Training";
string testDir = mainDirectory + "\\Testing";
int bitNumber = 64;
int NumPUFX = 3;
int NumPUFY = 3;
int numXORs = 4;
double MeanForAPUF = 0.0;
double VarianceForAPUF = 1.0;
//IPUF iPUF = new IPUF(NumPUFX, NumPUFY, bitNumber, MeanForAPUF, VarianceForAPUF);
XORArbiterPUF xPUF = new XORArbiterPUF(numXORs, bitNumber, MeanForAPUF, VarianceForAPUF);
DataGeneration.GenerateIPUFDataForKeras(xPUF, AppConstants.TrainingSize, trainDir);
DataGeneration.GenerateIPUFDataForKeras(xPUF, AppConstants.TestingSize, testDir);
MessageBox.Show("Data has been generated and saved successfully.");
}
//Attack different XOR APUFs (same type) in parallel
//public static void RepeatAttackOnePUFType()
//{
// int attackNumber = 40;
// double[] currentAccuracies = ClassicalAttackXORAPUFMulti(bitNumber, xorNumber, AppConstants.CoreNumber);
//}
//int bitNumber = 128;
//int xorNumber = 4;
//Runs attack multiple times, each time it is on a DIFFERENT XOR APUF
public static double[] ClassicalAttackXORAPUFMulti(int bitNumber, int numXOR, int attackRepeatNumber)
{
//Generate a PUF
double aPUFMean = 0.0;
double aPUFVar = 1.0;
double aPUFMeanNoise = 0.0;
double aPUFNoiseVar = 0.0;
//Create the XOR APUF for parallel runs
XORArbiterPUF xPUF = new XORArbiterPUF(numXOR, bitNumber, aPUFMean, aPUFVar, aPUFMeanNoise, aPUFNoiseVar);
XORArbiterPUF[] xArray = new XORArbiterPUF[attackRepeatNumber];
for (int i = 0; i < xArray.Length; i++)
{
xArray[i] = new XORArbiterPUF(numXOR, bitNumber, aPUFMean, aPUFVar, aPUFMeanNoise, aPUFNoiseVar);
}
sbyte[][] trainingData = new sbyte[AppConstants.TrainingSize][]; //these will be phi vectors
sbyte[][] allTrainingResponses = new sbyte[attackRepeatNumber][]; //first index PUF, second index sample
for (int i = 0; i < attackRepeatNumber; i++)
{
allTrainingResponses[i] = new sbyte[AppConstants.TrainingSize];
}
Random[] rGenArray = new Random[AppConstants.CoreNumber];
for (int i = 0; i < AppConstants.CoreNumber; i++)
{
rGenArray[i] = new Random((int)DateTime.Now.Ticks);
System.Threading.Thread.Sleep(10); //prevent the random number generators from being the same
}
DataGeneration.GenerateTrainingDataParallel(xArray, trainingData, allTrainingResponses, rGenArray);
Console.Out.WriteLine("Data Generation Complete.");
//create the objective function for parallel runs
ObjectiveFunctionResponseXOR[] rObjArray = new ObjectiveFunctionResponseXOR[attackRepeatNumber];
for (int i = 0; i < rObjArray.Length; i++)
{
rObjArray[i] = new ObjectiveFunctionResponseXOR();
}
double[][] solutionList = new double[attackRepeatNumber][];
Random[] randomGeneratorArray = new Random[attackRepeatNumber];
for (int r = 0; r < attackRepeatNumber; r++)
{
randomGeneratorArray[r] = new Random((int)DateTime.Now.Ticks);
System.Threading.Thread.Sleep(10); //prevent the random number generators from being the same
}
//time to save the invariant data
//if (AppConstants.IsLargeData == false)
//{
InvariantData invD = new InvariantData(trainingData, allTrainingResponses, xArray);
string dayString = System.DateTime.Today.ToString();
dayString = dayString.Replace(@"/", "-");
dayString = dayString.Replace(" ", string.Empty);
dayString = dayString.Replace(":", string.Empty);
string invariantDataFileName = "InvariantData" + dayString;
string fName = AppConstants.SaveDir + invariantDataFileName;
FileInfo fi = new FileInfo(fName);
Stream str = fi.Open(FileMode.OpenOrCreate, FileAccess.Write);
BinaryFormatter bf = new BinaryFormatter();
invD.Serialize(bf, str);
str.Close();
var watch = System.Diagnostics.Stopwatch.StartNew();
Parallel.For(0, attackRepeatNumber, a =>
{
Random randomGenerator = randomGeneratorArray[a]; //remove the dependences for parallelization
int dimensionNumber = (bitNumber + 1) * xArray[a].GetPUFNum(); //the weights of all the XOR APUFs
sbyte[] trainingResponse = allTrainingResponses[a];
//Generate the first solution randomly for CMA-ES
double[] firstSolution = new double[dimensionNumber];
for (int i = 0; i < firstSolution.Length; i++)
{
firstSolution[i] = randomGenerator.NextDouble();
}
Console.Out.WriteLine("Beginning CMA-ES run # " + a.ToString());
//CMAESCandidate solutionCMAES = CMAESMethods.ComputeCMAES(dimensionNumber, rObjArray[a], trainingData, trainingResponse, firstSolution, randomGenerator);
CMAESCandidate solutionCMAES = CMAESMethods.ComputeCMAESRecoverable(dimensionNumber, rObjArray[a], trainingData, trainingResponse, firstSolution, randomGenerator, a);
double solutionVal = solutionCMAES.GetObjectiveFunctionValue();
solutionList[a] = solutionCMAES.GetWeightVector(); //store the solution in independent memory
Console.Out.WriteLine("CMA-ES on core " + a.ToString() + " finished.");
});
watch.Stop();
Console.Out.WriteLine("Elapsed Time is " + watch.ElapsedMilliseconds.ToString());
//measure the accuracy
Random randomGenerator2 = new Random((int)DateTime.Now.Ticks);
double averageAccuracy = 0;
double[] solutionAccuracies = new double[attackRepeatNumber];
for (int a = 0; a < solutionList.Length; a++)
{
sbyte[][] testingData = new sbyte[AppConstants.TestingSize][]; //these will be phi vectors
sbyte[] testingResponse = new sbyte[AppConstants.TestingSize];
DataGeneration.GenerateTrainingData(xArray[a], testingData, testingResponse, randomGenerator2);
double accMeasures = rObjArray[0].ObjFunValue(solutionList[a], testingData, testingResponse);
solutionAccuracies[a] = accMeasures;
averageAccuracy = averageAccuracy + accMeasures;
}
averageAccuracy = averageAccuracy / (double)attackRepeatNumber;
Console.Out.WriteLine("The average accuracy for the XOR APUF is " + averageAccuracy.ToString());
return(solutionAccuracies);
}
//Runs the attack on one PUF model, the cores are used to evaluate the CRPs of one model (in parallel) so the method will run as fast as possible
public static double ClassicalAttackXORAPUFSingle(int bitNumber, int numXOR)
{
//Generate a PUF
double aPUFMean = 0.0;
double aPUFVar = 1.0;
double aPUFMeanNoise = 0.0;
double aPUFNoiseVar = 0.0;
//Create the XOR APUF
XORArbiterPUF xPUF = new XORArbiterPUF(numXOR, bitNumber, aPUFMean, aPUFVar, aPUFMeanNoise, aPUFNoiseVar);
//Arrays for storing the training data
sbyte[][] trainingData = new sbyte[AppConstants.TrainingSize][]; //these will be phi vectors
sbyte[][] allTrainingResponses = new sbyte[1][]; //first index PUF, second index sample
allTrainingResponses[0] = new sbyte[AppConstants.TrainingSize];
Random[] rGenArray = new Random[AppConstants.CoreNumber];
for (int i = 0; i < AppConstants.CoreNumber; i++)
{
rGenArray[i] = new Random((int)DateTime.Now.Ticks);
System.Threading.Thread.Sleep(10); //prevent the random number generators from being the same
}
DataGeneration.GenerateTrainingDataParallel(xPUF, trainingData, allTrainingResponses, rGenArray);
Console.Out.WriteLine("Data Generation Complete.");
//create the objective function for parallel runs
ObjectiveFunctionResponseXOR rObj = new ObjectiveFunctionResponseXOR();
//Start the attack run
var watch = System.Diagnostics.Stopwatch.StartNew();
Random randomGenerator = new Random((int)DateTime.Now.Ticks);;
int dimensionNumber = (bitNumber + 1) * xPUF.GetPUFNum(); //the weights of all the XOR APUFs
sbyte[] trainingResponse = allTrainingResponses[0];
//Generate the first solution randomly for CMA-ES
double[] firstSolution = new double[dimensionNumber];
for (int i = 0; i < firstSolution.Length; i++)
{
firstSolution[i] = randomGenerator.NextDouble();
}
Console.Out.WriteLine("Beginning CMA-ES");
//The next line uses maximum parallelism on a single run
CMAESCandidate solutionCMAES = CMAESMethods.ComputeCMAES(dimensionNumber, rObj, trainingData, trainingResponse, firstSolution, randomGenerator);
double solutionVal = solutionCMAES.GetObjectiveFunctionValue();
double[] computedSolution = solutionCMAES.GetWeightVector(); //store the solution in independent memory
Console.Out.WriteLine("CMA-ES finished.");
watch.Stop();
Console.Out.WriteLine("Elapsed Time is " + watch.ElapsedMilliseconds.ToString());
//turn off parallelism
AppConstants.UseParallelismOnSingleCMAES = false;
//measure the accuracy
Random randomGenerator2 = new Random((int)DateTime.Now.Ticks);
sbyte[][] testingData = new sbyte[AppConstants.TestingSize][]; //these will be phi vectors
sbyte[] testingResponse = new sbyte[AppConstants.TestingSize];
DataGeneration.GenerateTrainingData(xPUF, testingData, testingResponse, randomGenerator2);
double accMeasure = rObj.ObjFunValue(computedSolution, testingData, testingResponse);
Console.Out.WriteLine("The accuracy for the XOR APUF is " + accMeasure.ToString());
return(accMeasure);
}
public static double[] ClassicalAttackXORAPUFMultiRecovered(int bitNumber, int attackRepeatNumber, InvariantData invData, VariantData[] variantDataArray)
{
//Create the XOR APUF for parallel runs
XORArbiterPUF[] xArray = new XORArbiterPUF[attackRepeatNumber];
for (int i = 0; i < xArray.Length; i++)
{
xArray[i] = (XORArbiterPUF)invData.GetPUFatIndex(i);
}
sbyte[][] trainingData = invData.GetTrainingData(); //these will be phi vectors
sbyte[][] allTrainingResponses = invData.GetTrainingResponseAll(); //first index PUF, second index sample
//create the objective function for parallel runs
ObjectiveFunctionResponseXOR[] rObjArray = new ObjectiveFunctionResponseXOR[attackRepeatNumber];
for (int i = 0; i < rObjArray.Length; i++)
{
rObjArray[i] = new ObjectiveFunctionResponseXOR();
}
double[][] solutionList = new double[attackRepeatNumber][];
Random[] randomGeneratorArray = new Random[attackRepeatNumber];
for (int r = 0; r < attackRepeatNumber; r++)
{
randomGeneratorArray[r] = new Random((int)DateTime.Now.Ticks);
System.Threading.Thread.Sleep(10); //prevent the random number generators from being the same
}
var watch = System.Diagnostics.Stopwatch.StartNew();
Parallel.For(0, attackRepeatNumber, a =>
{
Random randomGenerator = randomGeneratorArray[a]; //remove the dependences for parallelization
int dimensionNumber = (bitNumber + 1) * xArray[a].GetPUFNum(); //the weights of all the XOR APUFs
sbyte[] trainingResponse = allTrainingResponses[a];
//Generate the first solution randomly for CMA-ES
double[] firstSolution = new double[dimensionNumber];
for (int i = 0; i < firstSolution.Length; i++)
{
firstSolution[i] = randomGenerator.NextDouble();
}
Console.Out.WriteLine("Beginning CMA-ES run # " + a.ToString());
//CMAESCandidate solutionCMAES = CMAESMethods.ComputeCMAES(dimensionNumber, rObjArray[a], trainingData, trainingResponse, firstSolution, randomGenerator);
CMAESCandidate solutionCMAES = CMAESMethods.RecoveredCMAES(randomGenerator, a, rObjArray[a], invData, variantDataArray[a]);
double solutionVal = solutionCMAES.GetObjectiveFunctionValue();
solutionList[a] = solutionCMAES.GetWeightVector(); //store the solution in independent memory
Console.Out.WriteLine("CMA-ES on core " + a.ToString() + " finished.");
//Console.Out.WriteLine("Final training value is "+solutionVal.ToString());
//}
});
watch.Stop();
Console.Out.WriteLine("Elapsed Time is " + watch.ElapsedMilliseconds.ToString());
//measure the accuracy
Random randomGenerator2 = new Random((int)DateTime.Now.Ticks);
double averageAccuracy = 0;
double[] solutionAccuracies = new double[attackRepeatNumber];
for (int a = 0; a < solutionList.Length; a++)
{
sbyte[][] testingData = new sbyte[AppConstants.TestingSize][]; //these will be phi vectors
sbyte[] testingResponse = new sbyte[AppConstants.TestingSize];
DataGeneration.GenerateTrainingData(xArray[a], testingData, testingResponse, randomGenerator2);
double accMeasures = rObjArray[0].ObjFunValue(solutionList[a], testingData, testingResponse);
solutionAccuracies[a] = accMeasures;
averageAccuracy = averageAccuracy + accMeasures;
}
averageAccuracy = averageAccuracy / (double)attackRepeatNumber;
Console.Out.WriteLine("The average accuracy for the XOR APUF is " + averageAccuracy.ToString());
return(solutionAccuracies);
}