ma_cisco_malware/main.py

78 lines
2.4 KiB
Python

import numpy as np
from keras.utils import np_utils
import dataset
import models
# 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():
# parameter
innerCNNFilters = 512
innerCNNKernelSize = 2
cnnDropout = 0.5
cnnHiddenDims = 1024
domainFeatures = 512
flowFeatures = 3
numCiscoFeatures = 30
windowSize = 10
maxLen = 40
embeddingSize = 100
kernel_size = 2
drop_out = 0.5
filters = 2
hidden_dims = 100
vocabSize = 40
threshold = 3
minFlowsPerUser = 10
numEpochs = 100
timesNeg = -1
char_dict = dataset.get_character_dict()
user_flow_df = dataset.get_user_flow_data()
print("create training dataset")
(X_tr, hits_tr, names_tr, server_tr, trusted_hits_tr) = dataset.create_dataset_from_flows(
user_flow_df, char_dict,
maxLen=maxLen, windowSize=windowSize)
# make client labels discrete with 4 different values
# TODO: use trusted_hits_tr for client classification too
client_labels = np.apply_along_axis(lambda x: dataset.discretize_label(x, 3), 0, np.atleast_2d(hits_tr))
# select only 1.0 and 0.0 from training data
pos_idx = np.where(client_labels == 1.0)[0]
neg_idx = np.where(client_labels == 0.0)[0]
idx = np.concatenate((pos_idx, neg_idx))
# select labels for prediction
client_labels = client_labels[idx]
server_labels = server_tr[idx]
# TODO: remove when features are flattened
for i in range(len(X_tr)):
X_tr[i] = X_tr[i][idx]
shared_cnn = models.get_shared_cnn(len(char_dict) + 1, embeddingSize, maxLen,
domainFeatures, kernel_size, domainFeatures, 0.5)
model = models.get_top_cnn(shared_cnn, flowFeatures, maxLen, windowSize, domainFeatures, filters, kernel_size,
cnnHiddenDims, cnnDropout)
model.compile(optimizer='adam',
loss='binary_crossentropy',
metrics=['accuracy'])
epochNumber = 0
client_labels = np_utils.to_categorical(client_labels, 2)
server_labels = np_utils.to_categorical(server_labels, 2)
model.fit(X_tr, [client_labels, server_labels], batch_size=128,
epochs=epochNumber + 1, shuffle=True, initial_epoch=epochNumber) # ,
# validation_data=(testData,testLabel))
if __name__ == "__main__":
main()