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#!/usr/bin/env python3
import base64
import binascii
import joblib
import multiprocessing as mp
import numpy as np
import os
import queue
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
LATENT_SIZE = 8
TRAINING_SIZE = 500
EPOCH_COUNT = 5
BATCH_SIZE = 10
def generate_autoencoder():
input_i = Input(shape=(), name='input_i')
input_e = Embedding(input_dim=INPUT_SIZE, output_dim=INPUT_SIZE, mask_zero=True, name='input_e')(input_i)
encoded_h1 = Dense(1024, activation='relu', name='encoded_h1')(input_e)
encoded_h2 = Dense(512, activation='relu', name='encoded_h2')(encoded_h1)
encoded_h3 = Dense(128, activation='relu', name='encoded_h3')(encoded_h2)
encoded_h4 = Dense(64, activation='relu', name='encoded_h4')(encoded_h3)
encoded_h5 = Dense(32, activation='relu', name='encoded_h5')(encoded_h4)
latent = Dense(LATENT_SIZE, activation='relu', name='latent')(encoded_h5)
input_l = Input(shape=(LATENT_SIZE), name='input_l')
decoder_h1 = Dense(32, activation='relu', name='decoder_h1')(input_l)
decoder_h2 = Dense(64, activation='relu', name='decoder_h2')(decoder_h1)
decoder_h3 = Dense(128, activation='relu', name='decoder_h3')(decoder_h2)
decoder_h4 = Dense(512, activation='relu', name='decoder_h4')(decoder_h3)
decoder_h5 = Dense(1024, activation='relu', name='decoder_h5')(decoder_h4)
output_i = Dense(INPUT_SIZE, activation='sigmoid', name='output_i')(decoder_h5)
encoder = Model(input_e, latent, name='encoder')
decoder = Model(input_l, output_i, name='decoder')
return encoder, decoder, Model(input_e, decoder(encoder(input_e)), name='VAE')
def compile_autoencoder():
encoder, decoder, autoencoder = generate_autoencoder()
autoencoder.compile(loss='mse', optimizer='adam', metrics=[tf.keras.metrics.Accuracy()])
return encoder, decoder, 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
shutdown_event, training_event, padded_pkts = global_user_data
if shutdown_event.is_set():
return False
try:
buf = base64.b64decode(json_dict['pkt'], validate=True)
except binascii.Error as err:
sys.stderr.write('\nBase64 Exception: {}\n'.format(str(err)))
sys.stderr.write('Affected JSON: {}\n'.format(str(json_dict)))
sys.stderr.flush()
return False
# 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], dtype='float64')
# Normalize the values
mat = mat.astype('float64') / 255.0
# Mean removal
matmean = np.mean(mat, dtype='float64')
mat -= matmean
# Pad resulting matrice
buf = preprocessing.sequence.pad_sequences(mat, padding="post", maxlen=INPUT_SIZE, truncating='post', dtype='float64')
padded_pkts.put(buf[0])
#print(list(buf[0]))
if not training_event.is_set():
sys.stdout.write('.')
sys.stdout.flush()
return True
def nDPIsrvd_worker(address, shared_shutdown_event, shared_training_event, shared_packet_list):
nsock = nDPIsrvdSocket()
try:
nsock.connect(address)
padded_pkts = list()
nsock.loop(onJsonLineRecvd, None, (shared_shutdown_event, shared_training_event, shared_packet_list))
except nDPIsrvd.SocketConnectionBroken as err:
sys.stderr.write('\nnDPIsrvd-Worker Socket Error: {}\n'.format(err))
except KeyboardInterrupt:
sys.stderr.write('\n')
except Exception as err:
sys.stderr.write('\nnDPIsrvd-Worker Exception: {}\n'.format(str(err)))
sys.stderr.flush()
shared_shutdown_event.set()
def keras_worker(load_model, save_model, shared_shutdown_event, shared_training_event, shared_packet_queue):
shared_training_event.set()
if load_model is not None:
sys.stderr.write('Loading model from {}\n'.format(load_model))
sys.stderr.flush()
try:
encoder, decoder, autoencoder = joblib.load(load_model)
except:
sys.stderr.write('Could not load model from {}\n'.format(load_model))
sys.stderr.write('Compiling new Autoencoder..\n')
sys.stderr.flush()
encoder, decoder, autoencoder = compile_autoencoder()
else:
encoder, decoder, autoencoder = compile_autoencoder()
decoder.summary()
encoder.summary()
autoencoder.summary()
shared_training_event.clear()
try:
packets = list()
while not shared_shutdown_event.is_set():
try:
packet = shared_packet_queue.get(timeout=1)
except queue.Empty:
packet = None
if packet is None:
continue
packets.append(packet)
if len(packets) % TRAINING_SIZE == 0:
shared_training_event.set()
print('\nGot {} packets, training..'.format(len(packets)))
tmp = np.array(packets)
x_test_encoded = encoder.predict(tmp, batch_size=BATCH_SIZE)
history = autoencoder.fit(
tmp, tmp, epochs=EPOCH_COUNT, batch_size=BATCH_SIZE,
validation_split=0.2,
shuffle=True
)
packets.clear()
shared_training_event.clear()
except KeyboardInterrupt:
sys.stderr.write('\n')
except Exception as err:
if len(str(err)) == 0:
err = 'Unknown'
sys.stderr.write('\nKeras-Worker Exception: {}\n'.format(str(err)))
sys.stderr.flush()
if save_model is not None:
sys.stderr.write('Saving model to {}\n'.format(save_model))
sys.stderr.flush()
joblib.dump([encoder, decoder, autoencoder], save_model)
try:
shared_shutdown_event.set()
except:
pass
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
mgr = mp.Manager()
shared_training_event = mgr.Event()
shared_training_event.clear()
shared_shutdown_event = mgr.Event()
shared_shutdown_event.clear()
shared_packet_queue = mgr.JoinableQueue()
nDPIsrvd_job = mp.Process(target=nDPIsrvd_worker, args=(
address,
shared_shutdown_event,
shared_training_event,
shared_packet_queue
))
nDPIsrvd_job.start()
keras_job = mp.Process(target=keras_worker, args=(
args.load_model,
args.save_model,
shared_shutdown_event,
shared_training_event,
shared_packet_queue
))
keras_job.start()
try:
shared_shutdown_event.wait()
except KeyboardInterrupt:
print('\nShutting down worker processess..')
nDPIsrvd_job.terminate()
nDPIsrvd_job.join()
keras_job.join()
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