ma_cisco_malware/models/pauls_networks.py

170 lines
6.4 KiB
Python

from collections import namedtuple
import keras
from keras.engine import Input, Model as KerasModel
from keras.layers import Conv1D, Dense, Dropout, Embedding, GlobalMaxPooling1D, TimeDistributed
import dataset
Model = namedtuple("Model", ["in_domains", "in_flows", "out_client", "out_server"])
best_config = {
"type": "paul",
"batch_size": 64,
"window_size": 10,
"domain_length": 40,
"flow_features": 3,
#
'dropout': 0.5,
'domain_features': 32,
'drop_out': 0.5,
'embedding_size': 64,
'filter_main': 512,
'flow_features': 3,
'dense_main': 32,
'filter_embedding': 32,
'hidden_embedding': 32,
'kernel_embedding': 8,
'kernels_main': 8,
'input_length': 40
}
def get_embedding(embedding_size, input_length, filter_size, kernel_size, hidden_dims, drop_out=0.5) -> KerasModel:
x = y = Input(shape=(input_length,))
y = Embedding(input_dim=dataset.get_vocab_size(), output_dim=embedding_size)(y)
y = Conv1D(filter_size,
kernel_size,
activation='relu')(y)
y = GlobalMaxPooling1D()(y)
y = Dropout(drop_out)(y)
y = Dense(hidden_dims, activation="relu")(y)
return KerasModel(x, y)
def get_model(cnnDropout, flow_features, window_size, domain_length, cnn_dims, kernel_size,
dense_dim, cnn) -> Model:
ipt_domains = Input(shape=(window_size, domain_length), name="ipt_domains")
encoded = TimeDistributed(cnn, name="domain_cnn")(ipt_domains)
ipt_flows = Input(shape=(window_size, flow_features), name="ipt_flows")
merged = keras.layers.concatenate([encoded, ipt_flows], -1)
# CNN processing a small slides of flow windows
y = Conv1D(cnn_dims,
kernel_size,
activation='relu')(merged)
# remove temporal dimension by global max pooling
y = GlobalMaxPooling1D()(y)
y = Dropout(cnnDropout)(y)
y = Dense(dense_dim, activation='relu')(y)
out_client = Dense(1, activation='sigmoid', name="client")(y)
out_server = Dense(1, activation='sigmoid', name="server")(y)
return Model(ipt_domains, ipt_flows, out_client, out_server)
def get_new_model(dropout, flow_features, window_size, domain_length, cnn_dims, kernel_size,
dense_dim, cnn) -> Model:
ipt_domains = Input(shape=(window_size, domain_length), name="ipt_domains")
ipt_flows = Input(shape=(window_size, flow_features), name="ipt_flows")
encoded = TimeDistributed(cnn, name="domain_cnn")(ipt_domains)
merged = keras.layers.concatenate([encoded, ipt_flows], -1)
y = Dense(dense_dim,
activation="relu",
name="dense_server")(merged)
out_server = Dense(1, activation="sigmoid", name="server")(y)
merged = keras.layers.concatenate([merged, y], -1)
# CNN processing a small slides of flow windows
y = Conv1D(cnn_dims,
kernel_size,
activation='relu')(merged)
# remove temporal dimension by global max pooling
y = GlobalMaxPooling1D()(y)
y = Dropout(dropout)(y)
y = Dense(dense_dim,
activation='relu',
name="dense_client")(y)
out_client = Dense(1, activation='sigmoid', name="client")(y)
return Model(ipt_domains, ipt_flows, out_client, out_server)
def get_server_model(flow_features, domain_length, dense_dim, cnn):
ipt_domains = Input(shape=(domain_length,), name="ipt_domains")
ipt_flows = Input(shape=(flow_features,), name="ipt_flows")
encoded = cnn(ipt_domains)
cnn.name = "domain_cnn"
merged = keras.layers.concatenate([encoded, ipt_flows], -1)
y = Dense(dense_dim,
activation="relu",
name="dense_server")(merged)
out_server = Dense(1, activation="sigmoid", name="server")(y)
return KerasModel(inputs=[ipt_domains, ipt_flows], outputs=out_server)
def get_new_model2(dropout, flow_features, window_size, domain_length, cnn_dims, kernel_size,
dense_dim, cnn) -> Model:
ipt_domains = Input(shape=(window_size, domain_length), name="ipt_domains")
ipt_flows = Input(shape=(window_size, flow_features), name="ipt_flows")
encoded = TimeDistributed(cnn, name="domain_cnn")(ipt_domains)
merged = keras.layers.concatenate([encoded, ipt_flows], -1)
y = Conv1D(cnn_dims,
kernel_size,
activation='relu')(merged)
# remove temporal dimension by global max pooling
y = GlobalMaxPooling1D()(y)
y = Dropout(dropout)(y)
y = Dense(dense_dim,
activation="relu",
name="dense_server")(y)
out_server = Dense(1, activation="sigmoid", name="server")(y)
# CNN processing a small slides of flow windows
y = Conv1D(cnn_dims,
kernel_size,
activation='relu')(merged)
# remove temporal dimension by global max pooling
y = GlobalMaxPooling1D()(y)
y = Dropout(dropout)(y)
y = Dense(dense_dim,
activation='relu',
name="dense_client")(y)
out_client = Dense(1, activation='sigmoid', name="client")(y)
return Model(ipt_domains, ipt_flows, out_client, out_server)
def get_new_soft(dropout, flow_features, window_size, domain_length, cnn_dims, kernel_size,
dense_dim, cnn) -> Model:
ipt_domains = Input(shape=(window_size, domain_length), name="ipt_domains")
ipt_flows = Input(shape=(window_size, flow_features), name="ipt_flows")
encoded = TimeDistributed(cnn, name="domain_cnn")(ipt_domains)
merged = keras.layers.concatenate([encoded, ipt_flows], -1)
y = conv_server = Conv1D(cnn_dims,
kernel_size,
activation='relu', name="conv_server")(merged)
# remove temporal dimension by global max pooling
y = GlobalMaxPooling1D()(y)
y = Dropout(dropout)(y)
y = dense_server = Dense(dense_dim,
activation="relu",
name="dense_server")(y)
out_server = Dense(1, activation="sigmoid", name="server")(y)
# CNN processing a small slides of flow windows
y = Conv1D(cnn_dims,
kernel_size,
activation='relu', name="conv_client")(merged)
# remove temporal dimension by global max pooling
y = GlobalMaxPooling1D()(y)
y = Dropout(dropout)(y)
y = Dense(dense_dim,
activation='relu',
name="dense_client")(y)
out_client = Dense(1, activation='sigmoid', name="client")(y)
return Model(ipt_domains, ipt_flows, out_client, out_server)