ma_cisco_malware/main.py

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import json
import logging
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import os
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import numpy as np
import pandas as pd
import tensorflow as tf
from keras.callbacks import ModelCheckpoint, CSVLogger, EarlyStopping
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from keras.models import load_model
import arguments
import dataset
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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)
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# create console handler and set level to debug
ch = logging.StreamHandler()
ch.setLevel(logging.DEBUG)
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# create formatter
formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
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# add formatter to ch
ch.setFormatter(formatter)
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# add ch to logger
logger.addHandler(ch)
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ch = logging.FileHandler("info.log")
ch.setLevel(logging.DEBUG)
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# create formatter
formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
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# add formatter to ch
ch.setFormatter(formatter)
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# 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)
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# 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,
'model_output': args.model_output
}
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def main_paul_best():
pauls_best_params = models.pauls_networks.best_config
main_train(pauls_best_params)
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def main_hyperband():
params = {
# static params
"type": ["paul"],
"batch_size": [args.batch_size],
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"window_size": [10],
"domain_length": [40],
"flow_features": [3],
"input_length": [40],
# model params
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"embedding_size": [8, 16, 32, 64, 128, 256],
"filter_embedding": [8, 16, 32, 64, 128, 256],
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"kernel_embedding": [1, 3, 5, 7, 9],
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"hidden_embedding": [8, 16, 32, 64, 128, 256],
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"dropout": [0.5],
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"domain_features": [8, 16, 32, 64, 128, 256],
"filter_main": [8, 16, 32, 64, 128, 256],
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"kernels_main": [1, 3, 5, 7, 9],
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"dense_main": [8, 16, 32, 64, 128, 256],
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}
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"))
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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)
if args.model_output == "both":
labels = [client_tr, server_tr]
elif args.model_output == "client":
labels = [client_tr]
elif args.model_output == "server":
labels = [server_tr]
model.fit([domain_tr, flow_tr],
labels,
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batch_size=args.batch_size,
epochs=args.epochs,
callbacks=callbacks,
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shuffle=True,
validation_split=0.2,
class_weight=custom_class_weights)
logger.info("save embedding")
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embedding.save(args.embedding_model)
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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"))
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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))
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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)
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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:
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main_train()
if "hyperband" == args.mode:
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main_hyperband()
if "test" == args.mode:
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main_test()
if "fancy" == args.mode:
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main_visualization()
if "paul" == args.mode:
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main_paul_best()
if "data" == args.mode:
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main_data()
if __name__ == "__main__":
main()