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#!/usr/bin/env python3
import base64
import joblib
import csv
import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
import sys
sys.path.append(os.path.dirname(sys.argv[0]) + '/../../dependencies')
sys.path.append(os.path.dirname(sys.argv[0]) + '/../share/nDPId')
sys.path.append(os.path.dirname(sys.argv[0]))
sys.path.append(sys.base_prefix + '/share/nDPId')
import nDPIsrvd
from nDPIsrvd import nDPIsrvdSocket, TermColor
INPUT_SIZE = nDPIsrvd.nDPId_PACKETS_PLEN_MAX
TRAINING_SIZE = 500
BATCH_SIZE = 10
def generate_autoencoder():
input_i = Input(shape=())
input_i = Embedding(input_dim=INPUT_SIZE, output_dim=INPUT_SIZE, mask_zero=True)(input_i)
encoded_h1 = Dense(1024, activation='relu', name='input_i')(input_i)
encoded_h2 = Dense(512, activation='relu', name='encoded_h1')(encoded_h1)
encoded_h3 = Dense(128, activation='relu', name='encoded_h2')(encoded_h2)
encoded_h4 = Dense(64, activation='relu', name='encoded_h3')(encoded_h3)
encoded_h5 = Dense(32, activation='relu', name='encoded_h4')(encoded_h4)
latent = Dense(2, activation='relu', name='encoded_h5')(encoded_h5)
decoder_h1 = Dense(32, activation='relu', name='latent')(latent)
decoder_h2 = Dense(64, activation='relu', name='decoder_h1')(decoder_h1)
decoder_h3 = Dense(128, activation='relu', name='decoder_h2')(decoder_h2)
decoder_h4 = Dense(512, activation='relu', name='decoder_h3')(decoder_h3)
decoder_h5 = Dense(1024, activation='relu', name='decoder_h4')(decoder_h4)
return input_i, Model(input_i, Dense(INPUT_SIZE, activation='sigmoid', name='decoder_h5')(decoder_h5))
def compile_autoencoder():
inp, autoencoder = generate_autoencoder()
autoencoder.compile(loss='mse', optimizer='adam', metrics=[tf.keras.metrics.Accuracy()])
return inp, autoencoder
def onJsonLineRecvd(json_dict, instance, current_flow, global_user_data):
if 'packet_event_name' not in json_dict:
return True
if json_dict['packet_event_name'] != 'packet' and \
json_dict['packet_event_name'] != 'packet-flow':
return True
_, padded_pkts = global_user_data
buf = base64.b64decode(json_dict['pkt'], validate=True)
# Generate decimal byte buffer with valus from 0-255
int_buf = []
for v in buf:
int_buf.append(int(v))
mat = np.array([int_buf])
# Normalize the values
mat = mat.astype('float32') / 255.
# Mean removal
matmean = np.mean(mat, axis=0)
mat -= matmean
# Pad resulting matrice
buf = preprocessing.sequence.pad_sequences(mat, padding="post", maxlen=INPUT_SIZE, truncating='post')
padded_pkts.append(buf[0])
sys.stdout.write('.')
sys.stdout.flush()
if (len(padded_pkts) % TRAINING_SIZE == 0):
print('\nGot {} packets, training..'.format(len(padded_pkts)))
tmp = np.array(padded_pkts)
history = autoencoder.fit(
tmp, tmp, epochs=10, batch_size=BATCH_SIZE,
validation_split=0.2,
shuffle=True
)
padded_pkts.clear()
#plot_model(autoencoder, show_shapes=True, show_layer_names=True)
#plt.plot(history.history['loss'])
#plt.plot(history.history['val_loss'])
#plt.title('model loss')
#plt.xlabel('loss')
#plt.ylabel('val_loss')
#plt.legend(['loss', 'val_loss'], loc='upper left')
#plt.show()
return True
if __name__ == '__main__':
sys.stderr.write('\b\n***************\n')
sys.stderr.write('*** WARNING ***\n')
sys.stderr.write('***************\n')
sys.stderr.write('\nThis is an unmature Autoencoder example.\n')
sys.stderr.write('Please do not rely on any of it\'s output!\n\n')
argparser = nDPIsrvd.defaultArgumentParser()
argparser.add_argument('--load-model', action='store',
help='Load a pre-trained model file.')
argparser.add_argument('--save-model', action='store',
help='Save the trained model to a file.')
argparser.add_argument('--training-size', action='store', type=int,
help='Set the amount of captured packets required to start the training phase.')
argparser.add_argument('--batch-size', action='store', type=int,
help='Set the batch size used for the training phase.')
args = argparser.parse_args()
address = nDPIsrvd.validateAddress(args)
TRAINING_SIZE = args.training_size if args.training_size is not None else TRAINING_SIZE
BATCH_SIZE = args.batch_size if args.batch_size is not None else BATCH_SIZE
sys.stderr.write('Recv buffer size: {}\n'.format(nDPIsrvd.NETWORK_BUFFER_MAX_SIZE))
sys.stderr.write('Connecting to {} ..\n'.format(address[0]+':'+str(address[1]) if type(address) is tuple else address))
sys.stderr.write('TRAINING_SIZE={}, BATCH_SIZE={}\n\n'.format(TRAINING_SIZE, BATCH_SIZE))
import tensorflow as tf
from tensorflow.keras import layers, preprocessing
from tensorflow.keras.layers import Embedding, Input, Dense
from tensorflow.keras.models import Model, Sequential
from tensorflow.keras.utils import plot_model
if args.load_model is not None:
sys.stderr.write('Loading model from {}\n'.format(args.load_model))
autoencoder, options = joblib.load(args.load_model)
else:
_, autoencoder = compile_autoencoder()
autoencoder.summary()
nsock = nDPIsrvdSocket()
nsock.connect(address)
try:
padded_pkts = list()
nsock.loop(onJsonLineRecvd, None, (autoencoder, padded_pkts))
except nDPIsrvd.SocketConnectionBroken as err:
sys.stderr.write('\n{}\n'.format(err))
except KeyboardInterrupt:
print()
if args.save_model is not None:
sys.stderr.write('Saving model to {}\n'.format(args.save_model))
joblib.dump([autoencoder, None], args.save_model)
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