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#!/usr/bin/env python3
import csv
import matplotlib.pyplot
import numpy
import os
import pandas
import sklearn
import sklearn.ensemble
import sklearn.inspection
import sys
import time
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
N_DIRS = 0
N_BINS = 0
ENABLE_FEATURE_IAT = True
ENABLE_FEATURE_PKTLEN = True
ENABLE_FEATURE_DIRS = True
ENABLE_FEATURE_BINS = True
def getFeatures(json):
return [json['flow_src_packets_processed'],
json['flow_dst_packets_processed'],
json['flow_src_tot_l4_payload_len'],
json['flow_dst_tot_l4_payload_len']]
def getFeaturesFromArray(json, expected_len=0):
if type(json) is str:
dirs = numpy.fromstring(json, sep=',', dtype=int)
dirs = numpy.asarray(dirs, dtype=int).tolist()
elif type(json) is list:
dirs = json
else:
raise TypeError('Invalid type: {}.'.format(type(json)))
if expected_len > 0 and len(dirs) != expected_len:
raise RuntimeError('Invalid array length; Expected {}, Got {}.'.format(expected_len, len(dirs)))
return dirs
def getRelevantFeaturesCSV(line):
ret = list()
ret.extend(getFeatures(line));
if ENABLE_FEATURE_IAT is True:
ret.extend(getFeaturesFromArray(line['iat_data'], N_DIRS - 1))
if ENABLE_FEATURE_PKTLEN is True:
ret.extend(getFeaturesFromArray(line['pktlen_data'], N_DIRS))
if ENABLE_FEATURE_DIRS is True:
ret.extend(getFeaturesFromArray(line['directions'], N_DIRS))
if ENABLE_FEATURE_BINS is True:
ret.extend(getFeaturesFromArray(line['bins_c_to_s'], N_BINS))
ret.extend(getFeaturesFromArray(line['bins_s_to_c'], N_BINS))
return [ret]
def getRelevantFeaturesJSON(line):
ret = list()
ret.extend(getFeatures(line))
if ENABLE_FEATURE_IAT is True:
ret.extend(getFeaturesFromArray(line['data_analysis']['iat']['data'], N_DIRS - 1))
if ENABLE_FEATURE_PKTLEN is True:
ret.extend(getFeaturesFromArray(line['data_analysis']['pktlen']['data'], N_DIRS))
if ENABLE_FEATURE_DIRS is True:
ret.extend(getFeaturesFromArray(line['data_analysis']['directions'], N_DIRS))
if ENABLE_FEATURE_BINS is True:
ret.extend(getFeaturesFromArray(line['data_analysis']['bins']['c_to_s'], N_BINS))
ret.extend(getFeaturesFromArray(line['data_analysis']['bins']['s_to_c'], N_BINS) )
return [ret]
def getRelevantFeatureNames():
names = list()
names.extend(['flow_src_packets_processed', 'flow_dst_packets_processed',
'flow_src_tot_l4_payload_len', 'flow_dst_tot_l4_payload_len'])
if ENABLE_FEATURE_IAT is True:
for x in range(N_DIRS - 1):
names.append('iat_{}'.format(x))
if ENABLE_FEATURE_PKTLEN is True:
for x in range(N_DIRS):
names.append('pktlen_{}'.format(x))
if ENABLE_FEATURE_DIRS is True:
for x in range(N_DIRS):
names.append('dirs_{}'.format(x))
if ENABLE_FEATURE_BINS is True:
for x in range(N_BINS):
names.append('bins_c_to_s_{}'.format(x))
for x in range(N_BINS):
names.append('bins_s_to_c_{}'.format(x))
return names
def plotPermutatedImportance(model, X, y):
result = sklearn.inspection.permutation_importance(model, X, y, n_repeats=10, random_state=42, n_jobs=-1)
forest_importances = pandas.Series(result.importances_mean, index=getRelevantFeatureNames())
fig, ax = matplotlib.pyplot.subplots()
forest_importances.plot.bar(yerr=result.importances_std, ax=ax)
ax.set_title("Feature importances using permutation on full model")
ax.set_ylabel("Mean accuracy decrease")
fig.tight_layout()
matplotlib.pyplot.show()
def onJsonLineRecvd(json_dict, instance, current_flow, global_user_data):
if 'flow_event_name' not in json_dict:
return True
if json_dict['flow_event_name'] != 'analyse':
return True
if 'ndpi' not in json_dict:
return True
if 'proto' not in json_dict['ndpi']:
return True
#print(json_dict)
model, = global_user_data
try:
X = getRelevantFeaturesJSON(json_dict)
y = model.predict(X)
s = model.score(X, y)
p = model.predict_log_proba(X)
print('DPI Engine detected: {:>24}, Prediction: {:>3}, Score: {}, Probabilities: {}'.format(
'"' + str(json_dict['ndpi']['proto']) + '"', '"' + str(y) + '"', s, p[0]))
except Exception as err:
print('Got exception `{}\'\nfor json: {}'.format(err, json_dict))
return True
def isProtoClass(proto_class, line):
s = line.lower()
for x in range(len(proto_class)):
if s.startswith(proto_class[x].lower()) is True:
return x + 1
return 0
if __name__ == '__main__':
argparser = nDPIsrvd.defaultArgumentParser()
argparser.add_argument('--csv', action='store', required=True,
help='Input CSV file generated with nDPIsrvd-analysed.')
argparser.add_argument('--proto-class', action='append', required=True,
help='nDPId protocol class of interest used for training and prediction. Can be specified multiple times. Example: tls.youtube')
argparser.add_argument('--generate-feature-importance', action='store_true',
help='Generates the permutated feature importance with matplotlib.')
argparser.add_argument('--enable-iat', action='store', default=True,
help='Use packet (I)nter (A)rrival (T)ime for learning and prediction.')
argparser.add_argument('--enable-pktlen', action='store', default=False,
help='Use layer 4 packet lengths for learning and prediction.')
argparser.add_argument('--enable-dirs', action='store', default=True,
help='Use packet directions for learning and prediction.')
argparser.add_argument('--enable-bins', action='store', default=True,
help='Use packet length distribution for learning and prediction.')
args = argparser.parse_args()
address = nDPIsrvd.validateAddress(args)
ENABLE_FEATURE_IAT = args.enable_iat
ENABLE_FEATURE_PKTLEN = args.enable_pktlen
ENABLE_FEATURE_DIRS = args.enable_dirs
ENABLE_FEATURE_BINS = args.enable_bins
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))
numpy.set_printoptions(formatter={'float_kind': "{:.1f}".format}, sign=' ')
numpy.seterr(divide = 'ignore')
sys.stderr.write('Learning via CSV..\n')
with open(args.csv, newline='\n') as csvfile:
reader = csv.DictReader(csvfile, delimiter=',', quotechar='"')
X = list()
y = list()
for line in reader:
N_DIRS = len(getFeaturesFromArray(line['directions']))
N_BINS = len(getFeaturesFromArray(line['bins_c_to_s']))
break
for line in reader:
try:
X += getRelevantFeaturesCSV(line)
y += [isProtoClass(args.proto_class, line['proto'])]
except RuntimeError as err:
print('Error: `{}\'\non line: {}'.format(err, line))
model = sklearn.ensemble.RandomForestClassifier()
model.fit(X, y)
if args.generate_feature_importance is True:
sys.stderr.write('Generating feature importance .. this may take some time')
plotPermutatedImportance(model, X, y)
print('Map[*] -> [0]')
for x in range(len(args.proto_class)):
print('Map["{}"] -> [{}]'.format(args.proto_class[x], x + 1))
sys.stderr.write('Predicting realtime traffic..\n')
nsock = nDPIsrvdSocket()
nsock.connect(address)
nsock.loop(onJsonLineRecvd, None, (model,))
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