/
nndpi_train.py
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/
nndpi_train.py
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import argparse
import numpy as np
import pandas as pd
import tensorflow as tf
from sklearn.model_selection import StratifiedShuffleSplit
from sklearn.preprocessing import LabelEncoder
from sklearn.utils import class_weight
from tensorflow.keras import callbacks
from tensorflow.keras.layers import (
GRU,
BatchNormalization,
Bidirectional,
Conv1D,
Dense,
Embedding,
Input,
MaxPooling1D,
)
from tensorflow.keras.models import Model
from tensorflow.keras.optimizers import Adam
def create_model(max_len, dropout_rate=0.05):
inp = Input(shape=(max_len))
x = Embedding(np.iinfo(np.uint8).max + 1, 1, input_length=max_len)(inp)
x = Conv1D(filters=64, kernel_size=25, activation="relu")(x)
x = BatchNormalization()(x)
x = MaxPooling1D(pool_size=4)(x)
x = Conv1D(filters=128, kernel_size=16, activation="relu")(x)
x = BatchNormalization()(x)
x = MaxPooling1D(pool_size=3)(x)
x = Conv1D(filters=256, kernel_size=5, activation="relu")(x)
x = BatchNormalization()(x)
x = MaxPooling1D(pool_size=2)(x)
x = Conv1D(filters=512, kernel_size=2, activation="relu")(x)
x = BatchNormalization()(x)
x = MaxPooling1D(pool_size=2)(x)
x = Bidirectional(
GRU(units=64, dropout=dropout_rate, recurrent_dropout=dropout_rate)
)(x)
x = Dense(64, activation="relu", kernel_initializer="glorot_normal")(x)
x = BatchNormalization()(x)
out1 = Dense(n_tag_1, activation="softmax", name="tag_1")(x)
out2 = Dense(n_tag_2, activation="softmax", name="tag_2")(x)
out3 = Dense(n_tag_3, activation="softmax", name="tag_3")(x)
out4 = Dense(n_tag_4, activation="softmax", name="tag_4")(x)
return Model(inputs=inp, outputs=[out1, out2, out3, out4])
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument(
"--multi_gpu", default=True, type=bool, help="Train on multi gpu system"
)
parser.add_argument(
"--batch_size", default=3072, type=np.uint32, help="Training batch_size"
)
parser.add_argument(
"--max_len",
default=1500,
type=np.uint32,
help="Preprocessed packet length in bytes",
)
parser.add_argument(
"--feather_df",
default="./CombinedPackets/allpkts.feather",
type=str,
help="Path to combined packets feather dataframe",
)
args = parser.parse_args()
print("Reading preprocessed packets..")
allpkts = pd.read_feather(args.feather_df, columns=[str(i) for i in range(1500)])
meta = pd.read_feather(
args.feather_df,
columns=["tag_1", "tag_2", "tag_3", "tag_4", "protocol", "filename", "ix"],
)
print("Encoding labels..")
le_1, le_2, le_3, le_4 = (
LabelEncoder(),
LabelEncoder(),
LabelEncoder(),
LabelEncoder(),
)
tag_1 = le_1.fit_transform(meta.tag_1)
tag_2 = le_2.fit_transform(meta.tag_2)
tag_3 = le_3.fit_transform(meta.tag_3)
tag_4 = le_4.fit_transform(meta.tag_4)
n_tag_1 = len(np.unique(tag_1))
n_tag_2 = len(np.unique(tag_2))
n_tag_3 = len(np.unique(tag_3))
n_tag_4 = len(np.unique(tag_4))
print("Creating stratified train/validation/test splits (80%, 10%, 10%)..")
sss = StratifiedShuffleSplit(n_splits=1, test_size=0.2, random_state=555)
train_index, test_valid_index = next(sss.split(allpkts, meta.filename))
sss = StratifiedShuffleSplit(n_splits=1, test_size=0.5, random_state=555)
test_index, val_index = next(
sss.split(allpkts.iloc[test_valid_index], meta.filename.iloc[test_valid_index])
)
test_index = allpkts.iloc[test_valid_index].iloc[test_index].index
val_index = allpkts.iloc[test_valid_index].iloc[val_index].index
print("Calculating class weigths..")
tag_1_class_weights = class_weight.compute_class_weight(
"balanced", np.unique(tag_1), tag_1
)
tag_2_class_weights = class_weight.compute_class_weight(
"balanced", np.unique(tag_2), tag_2
)
tag_3_class_weights = class_weight.compute_class_weight(
"balanced", np.unique(tag_3), tag_3
)
tag_4_class_weights = class_weight.compute_class_weight(
"balanced", np.unique(tag_4), tag_4
)
losses = {
"tag_1": "sparse_categorical_crossentropy",
"tag_2": "sparse_categorical_crossentropy",
"tag_3": "sparse_categorical_crossentropy",
"tag_4": "sparse_categorical_crossentropy",
}
class_weights = {
"tag_1": tag_1_class_weights,
"tag_2": tag_2_class_weights,
"tag_3": tag_3_class_weights,
"tag_4": tag_4_class_weights,
}
es = callbacks.EarlyStopping(
monitor="val_loss",
min_delta=0.0001,
patience=4,
verbose=1,
mode="min",
baseline=None,
restore_best_weights=True,
)
cp = callbacks.ModelCheckpoint(
"dpi.h5",
monitor="val_loss",
verbose=1,
save_best_only=True,
mode="min",
save_weights_only=False,
)
rlr = callbacks.ReduceLROnPlateau(
monitor="val_loss", factor=0.5, patience=2, min_lr=1e-7, mode="min", verbose=1
)
csv_logger = callbacks.CSVLogger("history.csv", separator=",", append=True)
if args.multi_gpu:
strategy = tf.distribute.MirroredStrategy(
cross_device_ops=tf.distribute.HierarchicalCopyAllReduce()
)
with strategy.scope():
model = create_model(max_len=args.max_len)
model.compile(loss=losses, optimizer=Adam(), metrics=["accuracy"])
else:
model = create_model(max_len=args.max_len)
model.compile(loss=losses, optimizer=Adam(), metrics=["accuracy"])
print("Training starting...")
model.fit(
x=allpkts.iloc[train_index].to_numpy(),
y={
"tag_1": tag_1[train_index],
"tag_2": tag_2[train_index],
"tag_3": tag_3[train_index],
"tag_4": tag_4[train_index],
},
validation_data=(
allpkts.iloc[val_index].to_numpy(),
{
"tag_1": tag_1[val_index],
"tag_2": tag_2[val_index],
"tag_3": tag_3[val_index],
"tag_4": tag_4[val_index],
},
),
class_weight=class_weights,
batch_size=args.batch_size,
callbacks=[es, cp, csv_logger, rlr],
epochs=100,
)