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