ma_cisco_malware/main.py

162 lines
5.5 KiB
Python

import argparse
from keras.utils import np_utils
import dataset
import models
parser = argparse.ArgumentParser()
# parser.add_argument("--modes", action="store", dest="modes", nargs="+")
parser.add_argument("--train", action="store", dest="train_data",
default="data/full_dataset.csv.tar.bz2")
parser.add_argument("--test", action="store", dest="test_data",
default="data/full_future_dataset.csv.tar.bz2")
# parser.add_argument("--h5data", action="store", dest="h5data",
# default="")
#
parser.add_argument("--model", action="store", dest="model",
default="model_x")
# parser.add_argument("--pred", action="store", dest="pred",
# default="")
#
# parser.add_argument("--type", action="store", dest="model_type",
# default="simple_conv")
#
parser.add_argument("--batch", action="store", dest="batch_size",
default=64, type=int)
parser.add_argument("--epochs", action="store", dest="epochs",
default=10, type=int)
# parser.add_argument("--samples", action="store", dest="samples",
# default=100000, type=int)
#
# parser.add_argument("--samples_val", action="store", dest="samples_val",
# default=10000, type=int)
#
parser.add_argument("--embd", action="store", dest="embedding",
default=128, type=int)
parser.add_argument("--hidden_char_dims", action="store", dest="hidden_char_dims",
default=256, type=int)
parser.add_argument("--window", action="store", dest="window",
default=10, type=int)
parser.add_argument("--domain_length", action="store", dest="domain_length",
default=40, type=int)
parser.add_argument("--domain_embd", action="store", dest="domain_embedding",
default=512, type=int)
# parser.add_argument("--queue", action="store", dest="queue_size",
# default=50, type=int)
#
# parser.add_argument("--p", action="store", dest="p_train",
# default=0.5, type=float)
#
# parser.add_argument("--p_val", action="store", dest="p_val",
# default=0.01, type=float)
#
# parser.add_argument("--gpu", action="store", dest="gpu",
# default=0, type=int)
#
# parser.add_argument("--tmp", action="store_true", dest="tmp")
#
# parser.add_argument("--test", action="store_true", dest="test")
args = parser.parse_args()
# 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 main_train():
# parameter
cnnDropout = 0.5
cnnHiddenDims = 512
kernel_size = 3
filters = 128
network = models.pauls_networks
char_dict = dataset.get_character_dict()
user_flow_df = dataset.get_user_flow_data(args.train_data)
print("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)
shared_cnn = network.get_embedding(len(char_dict) + 1, args.embedding, args.domain_length,
args.hidden_char_dims, kernel_size, args.domain_embedding, 0.5)
shared_cnn.summary()
model = network.get_model(cnnDropout, flow_tr.shape[-1], args.domain_embedding,
args.window, args.domain_length, filters, kernel_size,
cnnHiddenDims, shared_cnn)
model.summary()
model.compile(optimizer='adam',
loss='binary_crossentropy',
metrics=['accuracy'])
client_tr = np_utils.to_categorical(client_tr, 2)
server_tr = np_utils.to_categorical(server_tr, 2)
model.fit([domain_tr, flow_tr],
[client_tr, server_tr],
batch_size=args.batch_size,
epochs=args.epochs,
shuffle=True,
validation_split=0.2)
def main_test():
char_dict = dataset.get_character_dict()
user_flow_df = dataset.get_user_flow_data(args.test_data)
domain_val, flow_val, client_val, server_val = dataset.create_dataset_from_flows(
user_flow_df, char_dict,
max_len=args.domain_length, window_size=args.window)
# TODO: get model and exec model.evaluate(...)
def main_visualization():
mask = dataset.load_mask_eval(args.data, args.test_image)
y_pred_path = args.model_path + "pred.npy"
print("plot model")
model = load_model(args.model_path + "model.h5",
custom_objects=evaluation.get_metrics())
visualize.plot_model(model, args.model_path + "model.png")
print("plot training curve")
logs = pd.read_csv(args.model_path + "train.log")
visualize.plot_training_curve(logs, "{}/train.png".format(args.model_path))
pred = np.load(y_pred_path)
print("plot pr curve")
visualize.plot_precision_recall(mask, pred, "{}/prc.png".format(args.model_path))
visualize.plot_precision_recall_curves(mask, pred, "{}/prc2.png".format(args.model_path))
print("plot roc curve")
visualize.plot_roc_curve(mask, pred, "{}/roc.png".format(args.model_path))
print("store prediction image")
visualize.save_image_as(pred, "{}/pred.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)
def main():
main_train()
if __name__ == "__main__":
main()