os.path.dirname(os.path.realpath(__file__)) + "/../../../hybrid")
sys.path.append(os.path.dirname(os.path.realpath(__file__)) + "/../../..")
sys.path.append(os.path.dirname(os.path.realpath(__file__)) + "/../../../knn")
from cross_validation import CrossValidation
from preprocessor import Preprocessor
from dataSet import DataSet
from knn_classifier import KnnClassifier
from rna_classifier import RnaClassifier
from hybrid_classifier import HybridClassifier
from rna_module import RnaModule
from knn_module import KnnModule
from evaluate_module import EvaluateModule
dts = DataSet()
dts.setFilePath("bases/sub_bases/")
#CONFIGURACAO DA REDE NEURAL
rna = RnaModule()
rna.setNumberNeuronsImputLayer(30)
rna.setActivationFunctionImputLayer("tanh")
rna.setImputDimNeurons(30)
rna.setNumberNeuronsHiddenLayer(31)
rna.setActivationFunctionHiddenLayer("tanh")
rna.setNumberNeuronsOutputLayer(1)
rna.setActivationFunctionOutputLayer("tanh")
rna_classifier = RnaClassifier()
rna_classifier.setRna(rna)
#PREPROCESSADOR PARA ATRIBUTOS CATEGORICOS
preprocessor = Preprocessor()
sys.path.append(os.path.dirname(os.path.realpath(__file__)) + "/rna")
sys.path.append(os.path.dirname(os.path.realpath(__file__)) + "/hybrid")
sys.path.append(os.path.dirname(os.path.realpath(__file__)) + "/knn")
from cross_validation import CrossValidation
from preprocessor import Preprocessor
from dataSet import DataSet
from knn_classifier import KnnClassifier
from rna_classifier import RnaClassifier
from hybrid_classifier import HybridClassifier
from rna_module import RnaModule
from knn_module import KnnModule
from evaluate_module import EvaluateModule
dts = DataSet()
dts.setFilePath("bases/gstav_first_attempt/")
dts.setFileName("testes/base_iris.csv")
#dts.setFileName("SmallTrainingSet.csv")
#dts.setFileName("winequality-red.csv")
#dts.setFileName("NSL_KDD-master/20PercentTrainingSet.csv")
#dts.setFileName("NSL_KDD-master/KDDTrain+binary_class.csv")
#dts.setFileName("NSL_KDD-master/SmallTrainingSet.csv")
#dts.setFileName("NSL_KDD-master/SmallTrainingSetFiveClass.csv")
#dts.setFileName("../../KDDCUP99/kddcup10%.csv")
#print("load data")
dts.loadData(6)
import os
from dataSet import DataSet
arquivo = open("saidaaa.csv", 'w')
arquivo.write("CHAMOU PARTITION")
arquivo.close()
dts = DataSet()
print("Iniciando particionamento....")
arquivo = open("saidaaa.csv", 'a')
arquivo.write("Iniciando particionamento....A")
arquivo.close()
#pasta pra salvar
#dts.setFilePath("../../Bases/MachineLearningCVE/teste/")
dts.setFilePath("../../Bases/MachineLearningCVE/DoS_56att2/")
#caminho e nome do arquivo
#dts.setFileName("../../Bases/MachineLearningCVE/teste_ddos_BINARY.csv")
dts.setFileName(
"../../Bases/MachineLearningCVE/Friday-WorkingHours-Afternoon-DDos_BINARY_56att.pcap_ISCX.csv"
)
print("chamando load")
arquivo = open("saidaaa.csv", 'a')
arquivo.write("chamando load...A")
arquivo.close()
dts.loadData(10)
sys.path.append(os.path.dirname(os.path.realpath(__file__)) + "/rna")
sys.path.append(os.path.dirname(os.path.realpath(__file__)) + "/hybrid")
sys.path.append(os.path.dirname(os.path.realpath(__file__)) + "/knn")
from cross_validation import CrossValidation
from preprocessor import Preprocessor
from dataSet import DataSet
from knn_classifier import KnnClassifier
from rna_classifier import RnaClassifier
from hybrid_classifier import HybridClassifier
from rna_module import RnaModule
from knn_module import KnnModule
from evaluate_module import EvaluateModule
dts = DataSet()
dts.setFilePath("bases/sub_bases_nslkdd_20attribute/")
#dts.setFileName("base_iris.csv")
#dts.setFileName("SmallTrainingSet.csv")
##dts.setFileName("winequality-red.csv")
#dts.setFileName("NSL_KDD-master/20PercentTrainingSet.csv")
dts.setFileName("NSL_KDD-master/KDDTrain+binary_class.csv")
#dts.setFileName("NSL_KDD-master/SmallTrainingSet.csv")
#dts.setFileName("NSL_KDD-master/SmallTrainingSetFiveClass.csv")
#dts.setFileName("../../KDDCUP99/kddcup10%.csv")
#print("load data")
#ts.loadData(10)
#CONFIGURACAO DO KNN
knn = KnnModule()
splitter = StratifiedKFold(n_splits=10)
folds = []
for indexes in splitter.split(x, y):
folds.append(pd.DataFrame(dataset.values[indexes[1], ], columns=names))
return folds
def writeFoldToCsv(fold, foldIndex, destinationPath):
fold.to_csv(destinationPath + "fold_" + str(foldIndex) + ".csv",
index=False)
dts = DataSet()
dts.setFilePath("../cicids2017/10-folds/")
dts.setFileName("../cicids2017/total_selectedFeatures.csv")
dts.loadData()
directory = os.path.dirname(dts.file_path)
if not os.path.exists(directory):
os.makedirs(directory)
dataset = dts.dataframe_data_set
classFeatureName = dataset.columns[len(dataset.columns) - 1]
#removing all instances that have no class value
dataset = dts.dataframe_data_set.dropna(subset=[classFeatureName])
dataset = binarizeDataset(dataset, classFeatureName)