ma_cisco_malware/main.py

import argparse

import numpy as np
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("--data", action="store", dest="data",
#                     default="data/")
#
# 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("--area", action="store", dest="area_size",
#                     default=25, 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", dest="test_image",
#                     default=6, choices=range(7), type=int)

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():
    # 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

    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,
        max_len=maxLen, window_size=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'])

    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=args.batch_size,
              epochs=args.epochs,
              shuffle=True)
    # TODO: for validation we use future data -> validation_data=(testData,testLabel))


if __name__ == "__main__":
    main()