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)