ma_cisco_malware/main.py

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

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)

print = logger.info

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)


def exists_or_make_path(p):
    if not os.path.exists(p):
        os.makedirs(p)


def main_paul_best():
    char_dict = dataset.get_character_dict()
    pauls_best_params = models.pauls_networks.best_config
    pauls_best_params["vocab_size"] = len(char_dict) + 1
    main_train(pauls_best_params)


def main_hyperband():
    char_dict = dataset.get_character_dict()

    params = {
        # static params
        "type": ["paul"],
        "batch_size": [args.batch_size],
        "vocab_size": [len(char_dict) + 1],
        "window_size": [10],
        "domain_length": [40],
        "flow_features": [3],
        "input_length": [40],
        # model params
        "embedding_size": [16, 32, 64, 128, 256, 512],
        "filter_embedding": [16, 32, 64, 128, 256, 512],
        "kernel_embedding": [1, 3, 5, 7, 9],
        "hidden_embedding": [16, 32, 64, 128, 256, 512],
        "dropout": [0.5],
        "domain_features": [16, 32, 64, 128, 256, 512],
        "filter_main": [16, 32, 64, 128, 256, 512],
        "kernels_main": [1, 3, 5, 7, 9],
        "dense_main": [16, 32, 64, 128, 256, 512],
    }

    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):
    exists_or_make_path(args.model_path)

    char_dict = dataset.get_character_dict()
    domain_tr, flow_tr, client_tr, server_tr = load_or_generate_h5data(args.train_h5data, args.train_data,
                                                                       args.domain_length, args.window)

    # parameter
    p = {
        "type": "paul",
        "batch_size": 64,
        "window_size": args.window,
        "domain_length": args.domain_length,
        "flow_features": 3,
        "vocab_size": len(char_dict) + 1,
        #
        'dropout': 0.5,
        'domain_features': args.domain_embedding,
        'embedding_size': args.embedding,
        'filter_main': 128,
        'flow_features': 3,
        'dense_main': 512,
        'filter_embedding': args.hidden_char_dims,
        'hidden_embedding': args.domain_embedding,
        'kernel_embedding': 3,
        'kernels_main': 3,
        'input_length': 40
    }
    if not param:
        param = p

    embedding, model = models.get_models_by_params(param)
    embedding.summary()
    model.summary()
    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))
    if args.stop_early:
        callbacks.append(EarlyStopping(monitor='val_loss',
                                       patience=5,
                                       verbose=False))
    logger.info("compile model")
    custom_metrics = models.get_metric_functions()
    model.compile(optimizer='adam',
                  loss='categorical_crossentropy',
                  metrics=['accuracy'] + custom_metrics)
    logger.info("start training")
    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)
    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())
    stats = clf.evaluate([domain_val, flow_val],
                         [client_val, server_val],
                         batch_size=args.batch_size)
    # logger.info(f"loss: {loss}\nclient acc: {client_acc}\nserver acc: {server_acc}")
    logger.info(stats)
    y_pred = clf.predict([domain_val, flow_val],
                         batch_size=args.batch_size)
    np.save(args.future_prediction, y_pred)


def main_visualization():
    _, _, client_val, server_val = load_or_generate_h5data(args.test_h5data, args.test_data,
                                                           args.domain_length, args.window)
    logger.info("plot model")
    model = load_model(args.clf_model, custom_objects=models.get_metrics())
    visualize.plot_model(model, args.model_path + "model.png")
    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))

    client_pred, server_pred = np.load(args.future_prediction)
    logger.info("plot pr curve")
    visualize.plot_precision_recall(client_val.value, client_pred, "{}/client_prc.png".format(args.model_path))
    visualize.plot_precision_recall(server_val.value, server_pred, "{}/server_prc.png".format(args.model_path))
    visualize.plot_precision_recall_curves(client_val.value, client_pred, "{}/client_prc2.png".format(args.model_path))
    visualize.plot_precision_recall_curves(server_val.value, server_pred, "{}/server_prc2.png".format(args.model_path))
    logger.info("plot roc curve")
    visualize.plot_roc_curve(client_val.value, client_pred, "{}/client_roc.png".format(args.model_path))
    visualize.plot_roc_curve(server_val.value, server_pred, "{}/server_roc.png".format(args.model_path))


def main_score():
    # mask = dataset.load_mask_eval(args.data, args.test_image)
    # pred = np.load(args.pred)
    # visualize.score_model(mask, pred)
    pass


def main():
    if "train" in args.modes:
        main_train()
    if "hyperband" in args.modes:
        main_hyperband()
    if "test" in args.modes:
        main_test()
    if "fancy" in args.modes:
        main_visualization()
    if "score" in args.modes:
        main_score()
    if "paul" in args.modes:
        main_paul_best()


if __name__ == "__main__":
    main()