rene/ma_cisco_malware
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refactor main functions - separate things into different functions

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This commit is contained in:
René Knaebel
2017-07-07 08:43:16 +02:00
parent 933f6bf1d7
commit 3c4be52bb6
4 changed files with 143 additions and 12 deletions
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30
models/__init__.py
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@@ -1,2 +1,32 @@
from . import pauls_networks
from . import renes_networks
def get_models_by_params(params: dict):
# decomposing param section
# mainly embedding model
network_type = params.get("type")
vocab_size = params.get("vocab_size")
embedding_size = params.get("embedding_size")
input_length = params.get("input_length")
filter_embedding = params.get("filter_embedding")
kernel_embedding = params.get("kernel_embedding")
hidden_embedding = params.get("hidden_embedding")
dropout = params.get("dropout")
# mainly prediction model
flow_features = params.get("flow_features")
domain_features = params.get("domain_features")
window_size = params.get("window_size")
domain_length = params.get("domain_length")
filter_main = params.get("filter_main")
kernel_main = params.get("kernels_main")
dense_dim = params.get("dense_main")
# create models
networks = renes_networks if network_type == "rene" else pauls_networks
embedding_model = networks.get_embedding(vocab_size, embedding_size, input_length,
filter_embedding, kernel_embedding, hidden_embedding, drop_out=dropout)
predict_model = networks.get_model(dropout, flow_features, domain_features, window_size, domain_length,
filter_main, kernel_main, dense_dim, embedding_model)
return embedding_model, predict_model

9
models/renes_networks.py
View File

@@ -4,14 +4,14 @@ from keras.layers import Embedding, Conv1D, GlobalMaxPooling1D, Dense, Dropout,
def get_embedding(vocab_size, embedding_size, input_length,
hidden_char_dims, kernel_size, hidden_dims, drop_out=0.5):
filter_size, kernel_size, hidden_dims, drop_out=0.5):
x = y = Input(shape=(input_length,))
y = Embedding(input_dim=vocab_size, output_dim=embedding_size)(y)
y = Conv1D(hidden_char_dims, kernel_size=5, activation='relu')(y)
y = Conv1D(filter_size, kernel_size=5, activation='relu')(y)
y = MaxPool1D(pool_size=3, strides=1)(y)
y = Conv1D(hidden_char_dims, kernel_size=3, activation='relu')(y)
y = Conv1D(filter_size, kernel_size=3, activation='relu')(y)
y = MaxPool1D(pool_size=3, strides=1)(y)
y = Conv1D(hidden_char_dims, kernel_size=3, activation='relu')(y)
y = Conv1D(filter_size, kernel_size=3, activation='relu')(y)
y = GlobalMaxPooling1D()(y)
y = Dropout(drop_out)(y)
y = Dense(hidden_dims, activation="relu")(y)
@@ -35,6 +35,7 @@ def get_model(cnnDropout, flow_features, domain_features, window_size, domain_le
y = GlobalMaxPooling1D()(y)
y = Dropout(cnnDropout)(y)
y = Dense(dense_dim, activation='relu')(y)
y = Dense(dense_dim // 2, activation='relu')(y)
y1 = Dense(2, activation='softmax', name="client")(y)
y2 = Dense(2, activation='softmax', name="server")(y)