import json import logging import os import numpy as np import pandas as pd import tensorflow as tf from keras.callbacks import ModelCheckpoint, CSVLogger, EarlyStopping from keras.models import load_model import arguments import dataset import hyperband import models # create logger import visualize from dataset import load_or_generate_h5data from utils import exists_or_make_path, get_custom_class_weights logger = logging.getLogger('logger') logger.setLevel(logging.DEBUG) # create console handler and set level to debug ch = logging.StreamHandler() ch.setLevel(logging.DEBUG) # create formatter formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s') # add formatter to ch ch.setFormatter(formatter) # add ch to logger logger.addHandler(ch) ch = logging.FileHandler("info.log") ch.setLevel(logging.DEBUG) # create formatter formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s') # add formatter to ch ch.setFormatter(formatter) # add ch to logger logger.addHandler(ch) args = arguments.parse() if args.gpu: config = tf.ConfigProto(log_device_placement=True) config.gpu_options.per_process_gpu_memory_fraction = 0.5 config.gpu_options.allow_growth = True session = tf.Session(config=config) # default parameter PARAMS = { "type": args.model_type, "batch_size": 64, "window_size": args.window, "domain_length": args.domain_length, "flow_features": 3, # 'dropout': 0.5, 'domain_features': args.domain_embedding, 'embedding_size': args.embedding, 'filter_main': 64, 'flow_features': 3, # 'dense_main': 512, 'dense_main': 64, 'filter_embedding': args.hidden_char_dims, 'hidden_embedding': args.domain_embedding, 'kernel_embedding': 3, 'kernels_main': 3, 'input_length': 40 } def main_paul_best(): pauls_best_params = models.pauls_networks.best_config main_train(pauls_best_params) def main_hyperband(): params = { # static params "type": ["paul"], "batch_size": [args.batch_size], "window_size": [10], "domain_length": [40], "flow_features": [3], "input_length": [40], # model params "embedding_size": [8, 16, 32, 64, 128, 256], "filter_embedding": [8, 16, 32, 64, 128, 256], "kernel_embedding": [1, 3, 5, 7, 9], "hidden_embedding": [8, 16, 32, 64, 128, 256], "dropout": [0.5], "domain_features": [8, 16, 32, 64, 128, 256], "filter_main": [8, 16, 32, 64, 128, 256], "kernels_main": [1, 3, 5, 7, 9], "dense_main": [8, 16, 32, 64, 128, 256], } logger.info("create training dataset") domain_tr, flow_tr, client_tr, server_tr = load_or_generate_h5data(args.train_h5data, args.train_data, args.domain_length, args.window) hp = hyperband.Hyperband(params, [domain_tr, flow_tr], [client_tr, server_tr]) results = hp.run() json.dump(results, open("hyperband.json")) def main_train(param=None): logger.info(f"Create model path {args.model_path}") exists_or_make_path(args.model_path) logger.info(f"Use command line arguments: {args}") domain_tr, flow_tr, client_tr, server_windows_tr = load_or_generate_h5data(args.train_h5data, args.train_data, args.domain_length, args.window) if not param: param = PARAMS logger.info(f"Generator model with params: {param}") embedding, model, new_model = models.get_models_by_params(param) logger.info("define callbacks") callbacks = [] callbacks.append(ModelCheckpoint(filepath=args.clf_model, monitor='val_loss', verbose=False, save_best_only=True)) callbacks.append(CSVLogger(args.train_log)) logger.info(f"Use early stopping: {args.stop_early}") if args.stop_early: callbacks.append(EarlyStopping(monitor='val_loss', patience=5, verbose=False)) custom_metrics = models.get_metric_functions() server_tr = np.max(server_windows_tr, axis=1) if args.class_weights: logger.info("class weights: compute custom weights") custom_class_weights = get_custom_class_weights(client_tr.value, server_tr) logger.info(custom_class_weights) else: logger.info("class weights: set default") custom_class_weights = None logger.info(f"select model: {'new' if args.new_model else 'old'}") if args.new_model: server_tr = np.expand_dims(server_windows_tr, 2) model = new_model logger.info("compile and train model") embedding.summary() model.summary() model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'] + custom_metrics) model.fit([domain_tr, flow_tr], [client_tr, server_tr], batch_size=args.batch_size, epochs=args.epochs, callbacks=callbacks, shuffle=True, validation_split=0.2, class_weight=custom_class_weights) logger.info("save embedding") embedding.save(args.embedding_model) def main_test(): domain_val, flow_val, client_val, server_val = load_or_generate_h5data(args.test_h5data, args.test_data, args.domain_length, args.window) clf = load_model(args.clf_model, custom_objects=models.get_metrics()) c_pred, s_pred = clf.predict([domain_val, flow_val], batch_size=args.batch_size, verbose=1) dataset.save_predictions(args.future_prediction, c_pred, s_pred) def main_visualization(): domain_val, flow_val, client_val, server_val = load_or_generate_h5data(args.test_h5data, args.test_data, args.domain_length, args.window) client_val, server_val = client_val.value, server_val.value logger.info("plot model") model = load_model(args.clf_model, custom_objects=models.get_metrics()) visualize.plot_model(model, os.path.join(args.model_path, "model.png")) try: logger.info("plot training curve") logs = pd.read_csv(args.train_log) visualize.plot_training_curve(logs, "client", "{}/client_train.png".format(args.model_path)) visualize.plot_training_curve(logs, "server", "{}/server_train.png".format(args.model_path)) except Exception as e: logger.warning(f"could not generate training curves: {e}") client_pred, server_pred = dataset.load_predictions(args.future_prediction) client_pred, server_pred = client_pred.value, server_pred.value logger.info("plot pr curve") visualize.plot_precision_recall(client_val, client_pred.flatten(), "{}/client_prc.png".format(args.model_path)) # visualize.plot_precision_recall(server_val, server_pred, "{}/server_prc.png".format(args.model_path)) # visualize.plot_precision_recall_curves(client_val, client_pred, "{}/client_prc2.png".format(args.model_path)) # visualize.plot_precision_recall_curves(server_val, server_pred, "{}/server_prc2.png".format(args.model_path)) logger.info("plot roc curve") visualize.plot_roc_curve(client_val, client_pred.flatten(), "{}/client_roc.png".format(args.model_path)) # visualize.plot_roc_curve(server_val, server_pred, "{}/server_roc.png".format(args.model_path)) visualize.plot_confusion_matrix(client_val, client_pred.flatten().round(), "{}/client_cov.png".format(args.model_path), normalize=False, title="Client Confusion Matrix") # visualize.plot_confusion_matrix(server_val.argmax(1), server_pred.argmax(1), # "{}/server_cov.png".format(args.model_path), # normalize=False, title="Server Confusion Matrix") logger.info("visualize embedding") model = load_model(args.embedding_model) domain_encs, labels = dataset.load_or_generate_domains(args.test_data, args.domain_length) domain_embedding = model.predict(domain_encs, batch_size=args.batch_size, verbose=1) visualize.plot_embedding(domain_embedding, labels, path="{}/embd.png".format(args.model_path)) def main_data(): char_dict = dataset.get_character_dict() user_flow_df = dataset.get_user_flow_data(args.train_data) logger.info("create training dataset") domain_tr, flow_tr, client_tr, server_tr, _ = dataset.create_dataset_from_flows(user_flow_df, char_dict, max_len=args.domain_length, window_size=args.window) print(f"domain shape {domain_tr.shape}") print(f"flow shape {flow_tr.shape}") print(f"client shape {client_tr.shape}") print(f"server shape {server_tr.shape}") def main(): if "train" == args.mode: main_train() if "hyperband" == args.mode: main_hyperband() if "test" == args.mode: main_test() if "fancy" == args.mode: main_visualization() if "paul" == args.mode: main_paul_best() if "data" == args.mode: main_data() if __name__ == "__main__": main()