ma_cisco_malware/main.py

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import argparse
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import os
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from keras.models import load_model
from keras.utils import np_utils
import dataset
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import hyperband
import models
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parser = argparse.ArgumentParser()
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parser.add_argument("--modes", action="store", dest="modes", nargs="+",
default=[])
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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")
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# parser.add_argument("--h5data", action="store", dest="h5data",
# default="")
#
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parser.add_argument("--models", action="store", dest="models",
default="models/model_x")
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# parser.add_argument("--pred", action="store", dest="pred",
# default="")
#
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parser.add_argument("--type", action="store", dest="model_type",
default="paul")
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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)
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# 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")
#
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# parser.add_argument("--test", action="store_true", dest="test")
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args = parser.parse_args()
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args.embedding_model = args.models + "_embd.h5"
args.clf_model = args.models + "_clf.h5"
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args.h5data = args.train_data + ".h5"
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# 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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def exists_or_make_path(p):
if not os.path.exists(p):
os.makedirs(p)
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def main_paul_best():
char_dict = dataset.get_character_dict()
user_flow_df = dataset.get_user_flow_data(args.train_data)
param = models.pauls_networks.best_config
param["vocab_size"] = len(char_dict) + 1
print(param)
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)
client_tr = np_utils.to_categorical(client_tr, 2)
server_tr = np_utils.to_categorical(server_tr, 2)
embedding, model = models.get_models_by_params(param)
model.compile(optimizer='adam',
loss='categorical_crossentropy',
metrics=['accuracy'])
model.fit([domain_tr, flow_tr],
[client_tr, server_tr],
batch_size=args.batch_size,
epochs=args.epochs,
shuffle=True,
validation_split=0.2)
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embedding.save(args.embedding_model)
model.save(args.clf_model)
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def main_hyperband():
char_dict = dataset.get_character_dict()
user_flow_df = dataset.get_user_flow_data(args.train_data)
params = {
# static params
"type": ["paul"],
"batch_size": [64],
"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],
}
param = hyperband.sample_params(params)
print(param)
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)
hp = hyperband.Hyperband(params, [domain_tr, flow_tr], [client_tr, server_tr])
hp.run()
def main_train():
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exists_or_make_path(args.clf_model)
char_dict = dataset.get_character_dict()
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print("check for h5data")
try:
open(args.h5data, "r")
except FileNotFoundError:
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)
print("store training dataset as h5 file")
dataset.store_h5dataset(args.h5data, domain_tr, flow_tr, client_tr, server_tr)
print("load h5 dataset")
domain_tr, flow_tr, client_tr, server_tr = dataset.load_h5dataset(args.h5data)
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# parameter
param = {
"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
}
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embedding, model = models.get_models_by_params(param)
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embedding.summary()
model.summary()
model.compile(optimizer='adam',
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loss='categorical_crossentropy',
metrics=['accuracy'])
model.fit([domain_tr, flow_tr],
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[client_tr, server_tr],
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batch_size=args.batch_size,
epochs=args.epochs,
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shuffle=True,
validation_split=0.2)
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embedding.save(args.embedding_model)
model.save(args.clf_model)
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def main_test():
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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)
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# embedding = load_model(args.embedding_model)
clf = load_model(args.clf_model)
print(clf.evaluate([domain_val, flow_val],
[client_val, server_val],
batch_size=args.batch_size))
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():
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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()
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if "paul" in args.modes:
main_paul_best()
if __name__ == "__main__":
main()