ma_cisco_malware/models/__init__.py

105 lines
4.7 KiB
Python

from collections import namedtuple
from keras.models import Model
from . import networks
from .metrics import *
NetworkParameters = namedtuple("NetworkParameters", [
"type", "flow_features", "window_size", "domain_length", "output",
"embedding_size",
"domain_filter", "domain_kernel", "domain_dense", "domain_dropout",
"main_filter", "main_kernel", "main_dense", "main_dropout",
])
def create_model(model, output_type):
if output_type == "both":
return Model(inputs=[model.in_domains, model.in_flows], outputs=(model.out_client, model.out_server))
elif output_type == "client":
return Model(inputs=[model.in_domains, model.in_flows], outputs=(model.out_client,))
else:
raise Exception("unknown model output")
def get_models_by_params(params: dict):
K.clear_session()
# decomposing param section
# mainly embedding model
embedding_type = params.get("embedding_type", "small")
network_type = params.get("type")
# network_depth = params.get("depth")
embedding_size = params.get("embedding")
filter_embedding = params.get("filter_embedding")
kernel_embedding = params.get("kernel_embedding")
hidden_embedding = params.get("dense_embedding")
# dropout = params.get("dropout")
# mainly prediction model
flow_features = params.get("flow_features")
window_size = params.get("window_size")
domain_length = params.get("domain_length")
filter_main = params.get("filter_main")
kernel_main = params.get("kernel_main")
dense_dim = params.get("dense_main")
model_output = params.get("model_output", "both")
if embedding_type == "small":
domain_cnn = networks.get_domain_embedding_model(embedding_size, domain_length, filter_embedding,
kernel_embedding, hidden_embedding, 0.5)
elif embedding_type == "deep":
domain_cnn = networks.get_domain_embedding_model2(embedding_size, domain_length, filter_embedding,
kernel_embedding, hidden_embedding, 0.5)
else:
raise ValueError("embedding type not found")
if network_type == "final":
model = networks.get_final_model(0.25, flow_features, window_size, domain_length,
filter_main, kernel_main, dense_dim, domain_cnn)
model = create_model(model, model_output)
elif network_type in ("inter", "staggered"):
model = networks.get_inter_model(0.25, flow_features, window_size, domain_length,
filter_main, kernel_main, dense_dim, domain_cnn)
model = create_model(model, model_output)
elif network_type == "long":
model = networks.get_long_model(0.25, flow_features, window_size, domain_length,
filter_main, kernel_main, dense_dim, domain_cnn)
model = create_model(model, model_output)
elif network_type == "soft":
model = networks.get_long_model(0.25, flow_features, window_size, domain_length,
filter_main, kernel_main, dense_dim, domain_cnn)
model = create_model(model, model_output)
conv_server = model.get_layer("conv_server").trainable_weights
conv_client = model.get_layer("conv_client").trainable_weights
l1 = [0.001 * K.sum(K.abs(x - y)) for (x, y) in zip(conv_server, conv_client)]
model.add_loss(l1)
dense_server = model.get_layer("dense_server").trainable_weights
dense_client = model.get_layer("dense_client").trainable_weights
l2 = [0.001 * K.sum(K.abs(x - y)) for (x, y) in zip(dense_server, dense_client)]
model.add_loss(l2)
else:
raise ValueError("network type not found")
return model
def get_server_model_by_params(params: dict):
# decomposing param section
# mainly embedding model
network_depth = params.get("depth")
embedding_size = params.get("embedding")
input_length = params.get("input_length")
filter_embedding = params.get("filter_embedding")
kernel_embedding = params.get("kernel_embedding")
hidden_embedding = params.get("dense_embedding")
# mainly prediction model
flow_features = params.get("flow_features")
domain_length = params.get("domain_length")
dense_dim = params.get("dense_main")
embedding_model = networks.get_domain_embedding_model(embedding_size, input_length, filter_embedding,
kernel_embedding,
hidden_embedding, 0.5)
return networks.get_server_model(flow_features, domain_length, dense_dim, embedding_model)