static void ValidateModel(MarkovChain model, Dictionary <string, double> passwords, int guesses, bool hashed) //passwords here is not used...
{
Console.Write("Validating on {0} guesses... ", guesses);
PasswordCrackingEvaluator eval = new PasswordCrackingEvaluator(guesses, hashed);
var results = eval.Validate(model);
Console.WriteLine("Accounts: {0} Uniques: {1}", results._fitness, results._alternativeFitness);
}
static void ValidateForest(List <MarkovChain> models, Dictionary <string, PasswordInfo> passwords, int guesses, bool hashed) //passwords here is not used...
{
Console.WriteLine("Number of champion models: {0}", models.Count);
Console.WriteLine("Validating All-Star Team on {0} guesses each...", guesses);
PasswordCrackingEvaluator eval = new PasswordCrackingEvaluator(guesses, hashed);
eval.ValidatePopulation(models);
double accounts = eval.FoundValidationPasswords.Sum(s => PasswordCrackingEvaluator.Passwords[s].Accounts);
double uniques = eval.FoundValidationPasswords.Count;
Console.WriteLine("Accounts: {0} Uniques: {1}", accounts, uniques);
}
public NeatEvolutionAlgorithm <NeatGenome> CreateEvolutionAlgorithm(IGenomeFactory <NeatGenome> genomeFactory, List <NeatGenome> genomeList, IGenomeListEvaluator <NeatGenome> eval = null)
{
// Create distance metric. Mismatched genes have a fixed distance of 10; for matched genes the distance is their weigth difference.
IDistanceMetric distanceMetric = new ManhattanDistanceMetric(1.0, 0.0, 10.0);
ISpeciationStrategy <NeatGenome> speciationStrategy = new ParallelKMeansClusteringStrategy <NeatGenome>(distanceMetric, _parallelOptions);
// Create complexity regulation strategy.
IComplexityRegulationStrategy complexityRegulationStrategy = new NullComplexityRegulationStrategy();// ExperimentUtils.CreateComplexityRegulationStrategy(_complexityRegulationStr, _complexityThreshold);
// Create the evolution algorithm.
NeatEvolutionAlgorithm <NeatGenome> ea = new NeatEvolutionAlgorithm <NeatGenome>(_eaParams, speciationStrategy, complexityRegulationStrategy);
// Create the MC evaluator
PasswordCrackingEvaluator.Passwords = _passwords;
// Create genome decoder.
IGenomeDecoder <NeatGenome, MarkovChain> genomeDecoder = CreateGenomeDecoder();
// If we're running specially on Condor, skip this
if (eval == null)
{
_evaluator = new PasswordCrackingEvaluator(_guesses, Hashed);
// Create a genome list evaluator. This packages up the genome decoder with the genome evaluator.
// IGenomeListEvaluator<NeatGenome> innerEvaluator = new ParallelGenomeListEvaluator<NeatGenome, MarkovChain>(genomeDecoder, _evaluator, _parallelOptions);
IGenomeListEvaluator <NeatGenome> innerEvaluator = new ParallelNEATGenomeListEvaluator <NeatGenome, MarkovChain>(genomeDecoder, _evaluator, this);
/*
* // Wrap the list evaluator in a 'selective' evaulator that will only evaluate new genomes. That is, we skip re-evaluating any genomes
* // that were in the population in previous generations (elite genomes). This is determiend by examining each genome's evaluation info object.
* IGenomeListEvaluator<NeatGenome> selectiveEvaluator = new SelectiveGenomeListEvaluator<NeatGenome>(
* innerEvaluator,
* SelectiveGenomeListEvaluator<NeatGenome>.CreatePredicate_OnceOnly());
*/
// Initialize the evolution algorithm.
ea.Initialize(innerEvaluator, genomeFactory, genomeList);
}
else
{
// Initialize the evolution algorithm.
ea.Initialize(eval, genomeFactory, genomeList);
}
// Finished. Return the evolution algorithm
return(ea);
}
/// <summary>
/// Construct with the provided IGenomeDecoder, IPhenomeEvaluator, ParalleOptions and enablePhenomeCaching flag.
/// </summary>
public SerialGenomeListEvaluator(IGenomeDecoder <NeatGenome, MarkovChain> genomeDecoder,
PasswordCrackingEvaluator passwordCrackingEvaluator, bool hashed = false)
{
_genomeDecoder = genomeDecoder;
_passwordCrackingEvaluator = passwordCrackingEvaluator;
}
/// <summary>
/// Construct with the provided IGenomeDecoder and IPhenomeEvaluator.
/// Phenome caching is enabled by default.
/// The number of parallel threads defaults to Environment.ProcessorCount.
/// </summary>
public SerialGenomeListEvaluator(IGenomeDecoder <NeatGenome, MarkovChain> genomeDecoder,
PasswordCrackingEvaluator passwordCrackingEvaluator)
: this(genomeDecoder, passwordCrackingEvaluator, true)
{
}
// Runs a comparison of the two model types.
static void RunAllMarkovModelPairs(object special)
{
const string EXPERIMENT_OFFSET = @"..\..\..\experiments\intermediate\";
string[] models = new string[]
{
"first-order",
"8-layer"
};
// For every dataset, create a model
for (int i = 0; i < _datasetFilenames.Length; i++)
{
if (i != (int)special)
{
continue;
}
for (int m = 0; m < 2; m++)
{
int outputs;
string seedFile = EXPERIMENT_OFFSET + "seed-" + models[m] + "-" + _datasetFilenames[i].Name + ".xml";
Console.Write("Building {0} Markov model...", models[m]);
if (m == 0)
{
outputs = MarkovFilterCreator.GenerateFirstOrderMarkovFilter(seedFile, _passwords[i]);
}
else
{
outputs = MarkovFilterCreator.GenerateLayeredMarkovFilter(seedFile, _passwords[i], 8);
}
Console.WriteLine("Done! Outputs: {0}", outputs);
_experiment.OutputCount = outputs;
Console.WriteLine("Loading seed...");
var seed = _experiment.LoadPopulation(XmlReader.Create(seedFile))[0];
Console.WriteLine("Creating model...");
var model = _experiment.CreateGenomeDecoder().Decode(seed);
// For every dataset, test the model
for (int j = 0; j < _datasetFilenames.Length; j++)
{
Console.Write("Validating {0} {1} model on {2} with {3} guesses... ", models[m], _datasetFilenames[i].Name, _datasetFilenames[j].Name, VALIDATION_GUESSES);
PasswordCrackingEvaluator eval = new PasswordCrackingEvaluator(VALIDATION_GUESSES, false);
var results = eval.Validate(model, _passwords[j], EXPERIMENT_OFFSET + models[m] + "-" + _datasetFilenames[i].Name + "-" + _datasetFilenames[j].Name + ".csv", 10000);
// Console.WriteLine("Accounts: {0} Uniques: {1}", results._fitness, results._alternativeFitness);
Console.WriteLine("Total Score: {0} Uniques: {1}", results._fitness, results._alternativeFitness);
lock (_writerLock)
using (TextWriter writer = new StreamWriter(@"..\..\..\experiments\summary_results.csv", true))
writer.WriteLine("{0},{1},{2},{3},{4}%,{5}%",
_datasetFilenames[i].Name,
_datasetFilenames[j].Name,
results._fitness,
results._alternativeFitness,
results._fitness / (double)_passwords[j].Sum(kv => kv.Value) * 100,
results._alternativeFitness / (double)_passwords[j].Count * 100);
}
}
}
}
//static IGenomeDecoder<NeatGenome, MarkovChain> _genomeDecoder;
//static PasswordCrackingEvaluator _passwordCrackingEvaluator;
public static void Evaluate(IGenomeDecoder <NeatGenome, MarkovChain> genomeDecoder, PasswordCrackingEvaluator passwordCrackingEvaluator, PasswordEvolutionExperiment experiment)
{
string[] genomeFiles = Directory.GetFiles(@"..\..\..\experiments\genomes\", "*.xml");
XmlDocument doc = new XmlDocument();
int genomeNumber;
foreach (string genomeFile in genomeFiles)
{
// Read in genome
doc.Load(genomeFile);
//NeatGenome genome = NeatGenomeXmlIO.LoadGenome(doc, false);
//NeatGenomeFactory genomeFactory = (NeatGenomeFactory)CreateGenomeFactory();
NeatGenome genome = experiment.LoadPopulation(XmlReader.Create(genomeFile))[0];
MarkovChain phenome = experiment.CreateGenomeDecoder().Decode(genome);//genomeDecoder.Decode(genome);
string[] filePath = genomeFile.Split('\\');
string[] fileName = (filePath[filePath.Length - 1]).Split('-');
String fileNumber = (fileName[1]).Split('.')[0];
genomeNumber = Convert.ToInt32(fileNumber);
//FileStream fs = File.Open(@"..\..\..\experiments\genomes\genome-results\genome-"+genomeNumber+"-results.txt", FileMode.CreateNew, FileAccess.Write);
TextWriter tw = new StreamWriter(@"..\..\..\experiments\genomes\genome-results\genome-" + genomeNumber + "-results.txt");
// Evaluate
if (null == phenome)
{ // Non-viable genome.
tw.WriteLine("0.0 0.0");
}
else
{
FitnessInfo fitnessInfo = passwordCrackingEvaluator.Evaluate(phenome);
double val = fitnessInfo._fitness;
double val2 = fitnessInfo._alternativeFitness;
tw.WriteLine(fitnessInfo._fitness + " " + fitnessInfo._alternativeFitness);
}
tw.Close();
File.Create(@"..\..\..\experiments\genomes\genome-finished\genome-" + genomeNumber + "-finished.txt");
}
// Write results?? -> genome_#_results
// Write finished flag -> genome_#_finished
}
/// <summary>
/// Construct with the provided IGenomeDecoder and IPhenomeEvaluator.
/// Phenome caching is enabled by default.
/// The number of parallel threads defaults to Environment.ProcessorCount.
/// </summary>
public ParallelNEATGenomeListEvaluator(IGenomeDecoder <NeatGenome, MarkovChain> genomeDecoder,
PasswordCrackingEvaluator passwordCrackingEvaluator, PasswordEvolutionExperiment experiment)
: this(genomeDecoder, passwordCrackingEvaluator, true)
{
this.experiment = experiment;
}
