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#!/usr/bin/env python3
import base64
import csv
import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
import tensorflow as tf
import sys
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
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 = 100
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()
args = argparser.parse_args()
address = nDPIsrvd.validateAddress(args)
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))
_, autoencoder = compile_autoencoder()
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()
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