//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
}
//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>
/// Trains a Markov model on a the training set of passwords, then evolves it against the target password database
/// specified in the config file. At the end of the evolution, the champion model is evaluated for a larger number
/// of guesses.
/// </summary>
/// <param name="trainingSetFile">The file containing the passwords from which to build the initial Markov model.</param>
/// <param name="seedFile">The file to which the initial Markov model will be saved.</param>
/// <param name="configFile">The file containing all the configuration parameters of the evolution.</param>
/// <param name="resultsFile">The file to which the results will be saved at each generation.</param>
/// <param name="validateSeed">If true, the seed model will first be validated against a large number of guesses.</param>
//private static void RunExperiment(string trainingSetFile, string seedFile, string configFile, string resultsFile, bool validateSeed = false)
private static void RunExperiment(string configFile, bool validateSeed = false)
{
Console.Write("Building Markov model...");
// Load the XML configuration file
XmlDocument xmlConfig = new XmlDocument();
xmlConfig.Load(configFile);
XmlElement xmlConfigElement = xmlConfig.DocumentElement;
// Set Training File
string trainingSetFile = XmlUtils.GetValueAsString(xmlConfigElement, "TrainingFile");
// Create seedFile
string seedFile = XmlUtils.GetValueAsString(xmlConfigElement, "SeedFile");
// Create results file.
string resultsFile = XmlUtils.GetValueAsString(xmlConfigElement, "ResultsFile");
Console.WriteLine("\nTraining File: {0}\nSeed File: {1}\nResults File: {2}", trainingSetFile, seedFile, resultsFile);
// Load the training set passwords from file
var passwords = PasswordUtil.LoadPasswords(trainingSetFile, 8);
// Create a Markov model from the passwords. This model will be used
// as our seed for the evolution.
int outputs = MarkovFilterCreator.GenerateFirstOrderMarkovFilter(seedFile, passwords);
// Free up the memory used by the passwords
passwords = null;
Console.WriteLine("Done! Outputs: {0}", outputs);
_experiment = new PasswordEvolutionExperiment();
_experiment.OutputCount = outputs;
// Initialize the experiment with the specifications in the config file.
_experiment.Initialize("PasswordEvolution", xmlConfig.DocumentElement);
// Set the passwords to be used by the fitness evaluator.
// These are the passwords our models will try to guess.
// PasswordsWithAccounts is the file used for validation. Its account values won't be changed.
PasswordCrackingEvaluator.Passwords = _experiment.Passwords;
PasswordCrackingEvaluator.PasswordsWithAccounts = new Dictionary<string,double>(_experiment.Passwords); // Makes a deep copy
Console.WriteLine("Loading seed...");
// Load the seed model that we created at the start of this function
var seed = _experiment.LoadPopulation(XmlReader.Create(seedFile))[0];
// Validates the seed model by running it for a large number of guesses
if (validateSeed)
{
Console.WriteLine("Validating seed model...");
var seedModel = _experiment.CreateGenomeDecoder().Decode(seed);
ValidateModel(seedModel, _experiment.Passwords, VALIDATION_GUESSES, _experiment.Hashed);
}
// Create evolution algorithm using the seed model to initialize the population
Console.WriteLine("Creating population...");
_ea = _experiment.CreateEvolutionAlgorithm(seed);
// Attach an update event handler. This will be called at the end of every generation
// to log the progress of the evolution (see function logEvolutionProgress below).
_ea.UpdateEvent += new EventHandler(logEvolutionProgress);
//_ea.UpdateScheme = new UpdateScheme(1);//.UpdateMode.
// Setup results file
using (TextWriter writer = new StreamWriter(resultsFile))
writer.WriteLine("Generation,Champion Accounts,Champion Uniques,Average Accounts,Average Uniques,Total Accounts,Total Uniques");
_generationalResultsFile = resultsFile;
// Start algorithm (it will run on a background thread).
Console.WriteLine("Starting evolution. Pop size: {0} Guesses: {1}", _experiment.DefaultPopulationSize, _experiment.GuessesPerIndividual);
_ea.StartContinue();
// Wait until the evolution is finished.
while (_ea.RunState == RunState.Running) { Thread.Sleep(1000); }
// Validate the resulting model.
var decoder = _experiment.CreateGenomeDecoder();
var champ = decoder.Decode(_ea.CurrentChampGenome);
ValidateModel(champ, _experiment.Passwords, VALIDATION_GUESSES, _experiment.Hashed);
}
// 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);
}
}
}
}
/// <summary>
/// Trains a Markov model on a the training set of passwords, then evolves it against the target password database
/// specified in the config file. At the end of the evolution, the champion model is evaluated for a larger number
/// of guesses.
/// </summary>
/// <param name="trainingSetFile">The file containing the passwords from which to build the initial Markov model.</param>
/// <param name="seedFile">The file to which the initial Markov model will be saved.</param>
/// <param name="configFile">The file containing all the configuration parameters of the evolution.</param>
/// <param name="resultsFile">The file to which the results will be saved at each generation.</param>
/// <param name="validateSeed">If true, the seed model will first be validated against a large number of guesses.</param>
//private static void RunExperiment(string trainingSetFile, string seedFile, string configFile, string resultsFile, bool validateSeed = false)
private static void RunExperiment(string configFile, bool validateSeed = false)
{
Console.Write("Building Markov model...");
// Load the XML configuration file
XmlDocument xmlConfig = new XmlDocument();
xmlConfig.Load(configFile);
XmlElement xmlConfigElement = xmlConfig.DocumentElement;
// Set Training File
string trainingSetFile = XmlUtils.GetValueAsString(xmlConfigElement, "TrainingFile");
// Create seedFile
string seedFile = XmlUtils.GetValueAsString(xmlConfigElement, "SeedFile");
// Create results file.
string resultsFile = XmlUtils.GetValueAsString(xmlConfigElement, "ResultsFile");
Console.WriteLine("\nTraining File: {0}\nSeed File: {1}\nResults File: {2}", trainingSetFile, seedFile, resultsFile);
// Load the training set passwords from file
var passwords = PasswordUtil.LoadPasswords(trainingSetFile, 8);
// Create a Markov model from the passwords. This model will be used
// as our seed for the evolution.
int outputs = MarkovFilterCreator.GenerateFirstOrderMarkovFilter(seedFile, passwords);
// Free up the memory used by the passwords
passwords = null;
Console.WriteLine("Done! Outputs: {0}", outputs);
_experiment = new PasswordEvolutionExperiment();
_experiment.OutputCount = outputs;
// Initialize the experiment with the specifications in the config file.
_experiment.Initialize("PasswordEvolution", xmlConfig.DocumentElement);
// Set the passwords to be used by the fitness evaluator.
// These are the passwords our models will try to guess.
// PasswordsWithAccounts is the file used for validation. Its account values won't be changed.
PasswordCrackingEvaluator.Passwords = _experiment.Passwords;
PasswordCrackingEvaluator.PasswordsWithAccounts = new Dictionary <string, double>(_experiment.Passwords); // Makes a deep copy
Console.WriteLine("Loading seed...");
// Load the seed model that we created at the start of this function
var seed = _experiment.LoadPopulation(XmlReader.Create(seedFile))[0];
// Validates the seed model by running it for a large number of guesses
if (validateSeed)
{
Console.WriteLine("Validating seed model...");
var seedModel = _experiment.CreateGenomeDecoder().Decode(seed);
ValidateModel(seedModel, _experiment.Passwords, VALIDATION_GUESSES, _experiment.Hashed);
}
// Create evolution algorithm using the seed model to initialize the population
Console.WriteLine("Creating population...");
_ea = _experiment.CreateEvolutionAlgorithm(seed);
// Attach an update event handler. This will be called at the end of every generation
// to log the progress of the evolution (see function logEvolutionProgress below).
_ea.UpdateEvent += new EventHandler(logEvolutionProgress);
//_ea.UpdateScheme = new UpdateScheme(1);//.UpdateMode.
// Setup results file
using (TextWriter writer = new StreamWriter(resultsFile))
writer.WriteLine("Generation,Champion Accounts,Champion Uniques,Average Accounts,Average Uniques,Total Accounts,Total Uniques");
_generationalResultsFile = resultsFile;
// Start algorithm (it will run on a background thread).
Console.WriteLine("Starting evolution. Pop size: {0} Guesses: {1}", _experiment.DefaultPopulationSize, _experiment.GuessesPerIndividual);
_ea.StartContinue();
// Wait until the evolution is finished.
while (_ea.RunState == RunState.Running)
{
Thread.Sleep(1000);
}
// Validate the resulting model.
var decoder = _experiment.CreateGenomeDecoder();
var champ = decoder.Decode(_ea.CurrentChampGenome);
ValidateModel(champ, _experiment.Passwords, VALIDATION_GUESSES, _experiment.Hashed);
}
/// <summary>
/// Trains a Markov model on a the training set of passwords, then evolves it against the target password database
/// specified in the config file. At the end of the evolution, the champion model is evaluated for a larger number
/// of guesses.
/// </summary>
/// <param name="trainingSetFile">The file containing the passwords from which to build the initial Markov model.</param>
/// <param name="seedFile">The file to which the initial Markov model will be saved.</param>
/// <param name="configFile">The file containing all the configuration parameters of the evolution.</param>
/// <param name="resultsFile">The file to which the results will be saved at each generation.</param>
/// <param name="validateSeed">If true, the seed model will first be validated against a large number of guesses.</param>
//private static void RunExperiment(string trainingSetFile, string seedFile, string configFile, string resultsFile, bool validateSeed = false)
private static void RunExperiment(string configFile, bool validateSeed = false)
{
Console.WriteLine("Removing previous champions...");
string[] oldChampionFiles = Directory.GetFiles(@"../../../experiments/champions/", "*.xml");
foreach (string oldChampion in oldChampionFiles)
File.Delete(oldChampion);
Console.Write("Building Markov model...");
// Load the XML configuration file
XmlDocument xmlConfig = new XmlDocument();
xmlConfig.Load(configFile);
XmlElement xmlConfigElement = xmlConfig.DocumentElement;
// Set Training File
string trainingSetFile = XmlUtils.GetValueAsString(xmlConfigElement, "TrainingFile");
// Create seedFile
string seedFile = XmlUtils.GetValueAsString(xmlConfigElement, "SeedFile");
// Create results file.
string resultsFile = XmlUtils.GetValueAsString(xmlConfigElement, "ResultsFile");
Console.WriteLine();
Console.WriteLine("Training File: {0}", trainingSetFile);
Console.WriteLine("Seed File: {0}", seedFile);
Console.WriteLine("Results File: {0}", resultsFile);
// Load the training set passwords from file
var passwords = PasswordUtil.LoadPasswords(trainingSetFile, 8);
// Create a Markov model from the passwords. This model will be used
// as our seed for the evolution.
int outputs = MarkovFilterCreator.GenerateFirstOrderMarkovFilter(seedFile, passwords);
// Free up the memory used by the passwords
passwords = null;
Console.WriteLine("Done! Outputs: {0}", outputs);
_experiment = new PasswordEvolutionExperiment();
_experiment.OutputCount = outputs;
// Initialize the experiment with the specifications in the config file.
_experiment.Initialize("PasswordEvolution", xmlConfig.DocumentElement);
// Set the passwords to be used by the fitness evaluator.
// These are the passwords our models will try to guess.
// PasswordsWithAccounts is the file used for validation. Its account values won't be changed.
PasswordCrackingEvaluator.Passwords = _experiment.Passwords;
Console.WriteLine("Loading seed...");
// Load the seed model that we created at the start of this function
var seed = _experiment.LoadPopulation(XmlReader.Create(seedFile))[0];
// Validates the seed model by running it for a large number of guesses
if (validateSeed)
{
Console.WriteLine("Validating seed model...");
var seedModel = _experiment.CreateGenomeDecoder().Decode(seed);
ValidateModel(seedModel, _experiment.Passwords, VALIDATION_GUESSES, _experiment.Hashed);
}
// Create evolution algorithm using the seed model to initialize the population
Console.WriteLine("Creating population...");
_ea = _experiment.CreateEvolutionAlgorithm(seed);
// Attach an update event handler. This will be called at the end of every generation
// to log the progress of the evolution (see function logEvolutionProgress below).
_ea.UpdateEvent += new EventHandler(logEvolutionProgress);
//_ea.UpdateScheme = new UpdateScheme(1);//.UpdateMode.
// Setup results file
using (TextWriter writer = new StreamWriter(resultsFile))
writer.WriteLine("Generation,Champion Accounts,Champion Uniques,Average Accounts,Average Uniques,Total Accounts,Total Uniques");
_generationalResultsFile = resultsFile;
// Start algorithm (it will run on a background thread).
Console.WriteLine("Starting evolution. Pop size: {0} Guesses: {1}", _experiment.DefaultPopulationSize, _experiment.GuessesPerIndividual);
_ea.StartContinue();
// Wait until the evolution is finished.
while (_ea.RunState == RunState.Running) { Thread.Sleep(1000); }
if (VALIDATE_ALL_STAR)
{
// Validate the champions of each generation.
List<MarkovChain> championModels = new List<MarkovChain>();
string[] championFiles = Directory.GetFiles(@"../../../experiments/champions/", "*.xml");
foreach (string championFile in championFiles)
{
var currentChamp = _experiment.LoadPopulation(XmlReader.Create(championFile))[0];
var champModel = _experiment.CreateGenomeDecoder().Decode(currentChamp);
championModels.Add(champModel);
}
ValidateForest(championModels, _experiment.Passwords, VALIDATION_GUESSES/championFiles.Length, _experiment.Hashed);
// Validate a population made up of copies of the final champion.
/* List<MarkovChain> championCopyPop = new List<MarkovChain>();
Console.WriteLine();
Console.WriteLine("Validating the final champion population");
for (int i = 0; i < MAX_GENERATIONS; i++)
{
var decoder = _experiment.CreateGenomeDecoder();
var champ = decoder.Decode(_ea.CurrentChampGenome);
championCopyPop.Add(champ);
}
ValidateAllstarTeam(championCopyPop, _experiment.Passwords, VALIDATION_GUESSES, _experiment.Hashed);
*/
}
else
{
// Validate the resulting model.
var decoder = _experiment.CreateGenomeDecoder();
var champ = decoder.Decode(_ea.CurrentChampGenome);
ValidateModel(champ, _experiment.Passwords, VALIDATION_GUESSES, _experiment.Hashed);
}
}
static void Main(string[] args)
{
if (args.Length < 7)
{
Console.WriteLine("Usage: ModelEvaluator.exe <results_file> <model_id> <model_file> <finished_flag> <passwords_found_file> <config_file> <outputs> [passwords] [pw_length]");
return;
}
Console.WriteLine("Starting");
PasswordEvolutionExperiment experiment = new PasswordEvolutionExperiment();
int curArg = 0;
string resultsFile = args[curArg++];
int modelId = int.Parse(args[curArg++]);
string modelFile = args[curArg++];
string finishedFlag = args[curArg++];
string passwordsFoundFile = args[curArg++];
string configFile = args[curArg++];
int outputs = int.Parse(args[curArg++]);
experiment.OutputCount = outputs;
// Load the XML configuration file
XmlDocument xmlConfig = new XmlDocument();
xmlConfig.Load(configFile);
experiment.Initialize("evaluation", xmlConfig.DocumentElement);
// Optionally load the passwords from somewhere besides the file specified
// in the experiment config file.
if (args.Length > curArg)
{
Console.WriteLine("Passwords file: {0}", args[curArg]);
string passwordFile = args[curArg++];
int? pwLength = null;
if (args.Length > curArg)
{
Console.WriteLine("Password Length: {0}", args[curArg]);
pwLength = int.Parse(args[curArg++]);
}
// Load the passwords to evaluate with
experiment.Passwords = PasswordUtil.LoadPasswords(passwordFile, pwLength);
Console.WriteLine("Passwords loaded");
}
PasswordCrackingEvaluator.Passwords = experiment.Passwords;
PasswordCrackingEvaluator eval = new PasswordCrackingEvaluator(experiment.GuessesPerIndividual, experiment.Hashed);
var modelGenome = experiment.LoadPopulation(XmlReader.Create(modelFile))[0];
var model = experiment.CreateGenomeDecoder().Decode(modelGenome);
using (TextWriter tw = new StreamWriter(resultsFile))
{
// Evaluate
if (model == null)
{ // Non-viable genome.
tw.WriteLine("0.0 0.0");
}
else
{
FitnessInfo fitnessInfo = eval.Evaluate(model);
tw.WriteLine(fitnessInfo._fitness + " " + fitnessInfo._alternativeFitness);
}
}
using(TextWriter writer = new StreamWriter(passwordsFoundFile))
foreach(var pw in eval.FoundPasswords)
writer.WriteLine(pw);
File.Create(finishedFlag);
}
/// <summary>
/// Trains a Markov model on a the training set of passwords, then evolves it against the target password database
/// specified in the config file. At the end of the evolution, the champion model is evaluated for a larger number
/// of guesses.
/// </summary>
/// <param name="trainingSetFile">The file containing the passwords from which to build the initial Markov model.</param>
/// <param name="seedFile">The file to which the initial Markov model will be saved.</param>
/// <param name="configFile">The file containing all the configuration parameters of the evolution.</param>
/// <param name="resultsFile">The file to which the results will be saved at each generation.</param>
/// <param name="validateSeed">If true, the seed model will first be validated against a large number of guesses.</param>
//private static void RunExperiment(string trainingSetFile, string seedFile, string configFile, string resultsFile, bool validateSeed = false)
private static void RunExperiment(string configFile, bool validateSeed = false)
{
Console.WriteLine("Removing previous champions...");
string[] oldChampionFiles = Directory.GetFiles(@"../../../experiments/champions/", "*.xml");
foreach (string oldChampion in oldChampionFiles)
{
File.Delete(oldChampion);
}
Console.Write("Building Markov model...");
// Load the XML configuration file
XmlDocument xmlConfig = new XmlDocument();
xmlConfig.Load(configFile);
XmlElement xmlConfigElement = xmlConfig.DocumentElement;
// Set Training File
string trainingSetFile = XmlUtils.GetValueAsString(xmlConfigElement, "TrainingFile");
// Create seedFile
string seedFile = XmlUtils.GetValueAsString(xmlConfigElement, "SeedFile");
// Create results file.
string resultsFile = XmlUtils.GetValueAsString(xmlConfigElement, "ResultsFile");
Console.WriteLine();
Console.WriteLine("Training File: {0}", trainingSetFile);
Console.WriteLine("Seed File: {0}", seedFile);
Console.WriteLine("Results File: {0}", resultsFile);
// Load the training set passwords from file
var passwords = PasswordUtil.LoadPasswords(trainingSetFile, 8);
// Create a Markov model from the passwords. This model will be used
// as our seed for the evolution.
int outputs = MarkovFilterCreator.GenerateFirstOrderMarkovFilter(seedFile, passwords);
// Free up the memory used by the passwords
passwords = null;
Console.WriteLine("Done! Outputs: {0}", outputs);
_experiment = new PasswordEvolutionExperiment();
_experiment.OutputCount = outputs;
// Initialize the experiment with the specifications in the config file.
_experiment.Initialize("PasswordEvolution", xmlConfig.DocumentElement);
// Set the passwords to be used by the fitness evaluator.
// These are the passwords our models will try to guess.
// PasswordsWithAccounts is the file used for validation. Its account values won't be changed.
PasswordCrackingEvaluator.Passwords = _experiment.Passwords;
Console.WriteLine("Loading seed...");
// Load the seed model that we created at the start of this function
var seed = _experiment.LoadPopulation(XmlReader.Create(seedFile))[0];
// Validates the seed model by running it for a large number of guesses
if (validateSeed)
{
Console.WriteLine("Validating seed model...");
var seedModel = _experiment.CreateGenomeDecoder().Decode(seed);
ValidateModel(seedModel, _experiment.Passwords, VALIDATION_GUESSES, _experiment.Hashed);
}
// Create evolution algorithm using the seed model to initialize the population
Console.WriteLine("Creating population...");
_ea = _experiment.CreateEvolutionAlgorithm(seed);
// Attach an update event handler. This will be called at the end of every generation
// to log the progress of the evolution (see function logEvolutionProgress below).
_ea.UpdateEvent += new EventHandler(logEvolutionProgress);
//_ea.UpdateScheme = new UpdateScheme(1);//.UpdateMode.
// Setup results file
using (TextWriter writer = new StreamWriter(resultsFile))
writer.WriteLine("Generation,Champion Accounts,Champion Uniques,Average Accounts,Average Uniques,Total Accounts,Total Uniques");
_generationalResultsFile = resultsFile;
// Start algorithm (it will run on a background thread).
Console.WriteLine("Starting evolution. Pop size: {0} Guesses: {1}", _experiment.DefaultPopulationSize, _experiment.GuessesPerIndividual);
_ea.StartContinue();
// Wait until the evolution is finished.
while (_ea.RunState == RunState.Running)
{
Thread.Sleep(1000);
}
if (VALIDATE_ALL_STAR)
{
// Validate the champions of each generation.
List <MarkovChain> championModels = new List <MarkovChain>();
string[] championFiles = Directory.GetFiles(@"../../../experiments/champions/", "*.xml");
foreach (string championFile in championFiles)
{
var currentChamp = _experiment.LoadPopulation(XmlReader.Create(championFile))[0];
var champModel = _experiment.CreateGenomeDecoder().Decode(currentChamp);
championModels.Add(champModel);
}
ValidateForest(championModels, _experiment.Passwords, VALIDATION_GUESSES / championFiles.Length, _experiment.Hashed);
// Validate a population made up of copies of the final champion.
/* List<MarkovChain> championCopyPop = new List<MarkovChain>();
*
* Console.WriteLine();
* Console.WriteLine("Validating the final champion population");
* for (int i = 0; i < MAX_GENERATIONS; i++)
* {
* var decoder = _experiment.CreateGenomeDecoder();
* var champ = decoder.Decode(_ea.CurrentChampGenome);
* championCopyPop.Add(champ);
* }
* ValidateAllstarTeam(championCopyPop, _experiment.Passwords, VALIDATION_GUESSES, _experiment.Hashed);
*/
}
else
{
// Validate the resulting model.
var decoder = _experiment.CreateGenomeDecoder();
var champ = decoder.Decode(_ea.CurrentChampGenome);
ValidateModel(champ, _experiment.Passwords, VALIDATION_GUESSES, _experiment.Hashed);
}
}