#!/usr/bin/env python3
import csv
import joblib
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 = False
ENABLE_FEATURE_PKTLEN = False
ENABLE_FEATURE_DIRS = True
ENABLE_FEATURE_BINS = True
PROTO_CLASSES = None
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 isProtoClass(proto_class, line):
if type(proto_class) != list or type(line) != str:
raise TypeError('Invalid type: {}/{}.'.format(type(proto_class), type(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
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, proto_class, disable_colors = global_user_data
try:
X = getRelevantFeaturesJSON(json_dict)
y = model.predict(X)
p = model.predict_log_proba(X)
if y[0] <= 0:
y_text = 'n/a'
else:
y_text = proto_class[y[0] - 1]
color_start = ''
color_end = ''
pred_failed = False
if disable_colors is False:
if json_dict['ndpi']['proto'].lower().startswith(y_text) is True:
color_start = TermColor.BOLD
color_end = TermColor.END
elif y_text not in proto_class and \
json_dict['ndpi']['proto'].lower() not in proto_class:
pass
else:
pred_failed = True
color_start = TermColor.WARNING + TermColor.BOLD
color_end = TermColor.END
probs = str()
for i in range(len(p[0])):
if json_dict['ndpi']['proto'].lower().startswith(proto_class[i - 1]) and disable_colors is False:
probs += '{}{:>2.1f}{}, '.format(TermColor.BOLD + TermColor.BLINK if pred_failed is True else '',
p[0][i], TermColor.END)
elif i == y[0]:
probs += '{}{:>2.1f}{}, '.format(color_start, p[0][i], color_end)
else:
probs += '{:>2.1f}, '.format(p[0][i])
probs = probs[:-2]
print('DPI Engine detected: {}{:>24}{}, Predicted: {}{:>24}{}, Probabilities: {}'.format(
color_start, json_dict['ndpi']['proto'].lower(), color_end,
color_start, y_text, color_end, probs))
if pred_failed is True:
pclass = isProtoClass(args.proto_class, json_dict['ndpi']['proto'].lower())
if pclass == 0:
msg = 'false positive'
else:
msg = 'false negative'
print('{:>46} {}{}{}'.format('[-]', TermColor.FAIL + TermColor.BOLD + TermColor.BLINK, msg, TermColor.END))
except Exception as err:
print('Got exception `{}\'\nfor json: {}'.format(err, json_dict))
return True
if __name__ == '__main__':
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('--csv', action='store',
help='Input CSV file generated with nDPIsrvd-analysed.')
argparser.add_argument('--proto-class', action='append', required=False,
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_true', default=None,
help='Enable packet (I)nter (A)rrival (T)ime for learning and prediction.')
argparser.add_argument('--enable-pktlen', action='store_true', default=None,
help='Enable layer 4 packet lengths for learning and prediction.')
argparser.add_argument('--disable-dirs', action='store_true', default=None,
help='Disable packet directions for learning and prediction.')
argparser.add_argument('--disable-bins', action='store_true', default=None,
help='Disable packet length distribution for learning and prediction.')
argparser.add_argument('--disable-colors', action='store_true', default=False,
help='Disable any coloring.')
argparser.add_argument('--sklearn-jobs', action='store', type=int, default=1,
help='Number of sklearn processes during training.')
argparser.add_argument('--sklearn-estimators', action='store', type=int, default=1000,
help='Number of trees in the forest.')
argparser.add_argument('--sklearn-min-samples-leaf', action='store', type=int, default=0.0001,
help='The minimum number of samples required to be at a leaf node.')
argparser.add_argument('--sklearn-class-weight', default='balanced', const='balanced', nargs='?',
choices=['balanced', 'balanced_subsample'],
help='Weights associated with the protocol classes.')
argparser.add_argument('--sklearn-max-features', default='sqrt', const='sqrt', nargs='?',
choices=['sqrt', 'log2'],
help='The number of features to consider when looking for the best split.')
argparser.add_argument('--sklearn-max-depth', action='store', type=int, default=128,
help='The maximum depth of a tree.')
argparser.add_argument('--sklearn-verbosity', action='store', type=int, default=0,
help='Controls the verbosity of sklearn\'s random forest classifier.')
args = argparser.parse_args()
address = nDPIsrvd.validateAddress(args)
if args.csv is None and args.load_model is None:
sys.stderr.write('{}: Either `--csv` or `--load-model` required!\n'.format(sys.argv[0]))
sys.exit(1)
if args.csv is None and args.generate_feature_importance is True:
sys.stderr.write('{}: `--generate-feature-importance` requires `--csv`.\n'.format(sys.argv[0]))
sys.exit(1)
if args.proto_class is None or len(args.proto_class) == 0:
if args.csv is None and args.load_model is None:
sys.stderr.write('{}: `--proto-class` missing, no useful classification can be performed.\n'.format(sys.argv[0]))
else:
if args.load_model is not None:
sys.stderr.write('{}: `--proto-class` set, but you want to load an existing model.\n'.format(sys.argv[0]))
sys.exit(1)
if args.load_model is not None:
sys.stderr.write('{}: You are loading an existing model file. ' \
'Some --sklearn-* command line parameters won\'t have any effect!\n'.format(sys.argv[0]))
if args.enable_iat is not None:
sys.stderr.write('{}: `--enable-iat` set, but you want to load an existing model.\n'.format(sys.argv[0]))
sys.exit(1)
if args.enable_pktlen is not None:
sys.stderr.write('{}: `--enable-pktlen` set, but you want to load an existing model.\n'.format(sys.argv[0]))
sys.exit(1)
if args.disable_dirs is not None:
sys.stderr.write('{}: `--disable-dirs` set, but you want to load an existing model.\n'.format(sys.argv[0]))
sys.exit(1)
if args.disable_bins is not None:
sys.stderr.write('{}: `--disable-bins` set, but you want to load an existing model.\n'.format(sys.argv[0]))
sys.exit(1)
ENABLE_FEATURE_IAT = args.enable_iat if args.enable_iat is not None else ENABLE_FEATURE_IAT
ENABLE_FEATURE_PKTLEN = args.enable_pktlen if args.enable_pktlen is not None else ENABLE_FEATURE_PKTLEN
ENABLE_FEATURE_DIRS = args.disable_dirs if args.disable_dirs is not None else ENABLE_FEATURE_DIRS
ENABLE_FEATURE_BINS = args.disable_bins if args.disable_bins is not None else ENABLE_FEATURE_BINS
PROTO_CLASSES = args.proto_class
numpy.set_printoptions(formatter={'float_kind': "{:.1f}".format}, sign=' ')
numpy.seterr(divide = 'ignore')
if args.proto_class is not None:
for i in range(len(args.proto_class)):
args.proto_class[i] = args.proto_class[i].lower()
if args.load_model is not None:
sys.stderr.write('Loading model from {}\n'.format(args.load_model))
model, options = joblib.load(args.load_model)
ENABLE_FEATURE_IAT, ENABLE_FEATURE_PKTLEN, ENABLE_FEATURE_DIRS, ENABLE_FEATURE_BINS, args.proto_class = options
if args.csv is not None:
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)
except RuntimeError as err:
print('Runtime Error: `{}\'\non line {}: {}'.format(err, reader.line_num - 1, line))
continue
except TypeError as err:
print('Type Error: `{}\'\non line {}: {}'.format(err, reader.line_num - 1, line))
continue
try:
y += [isProtoClass(args.proto_class, line['proto'])]
except TypeError as err:
X.pop()
print('Type Error: `{}\'\non line {}: {}'.format(err, reader.line_num - 1, line))
continue
sys.stderr.write('CSV data set contains {} entries.\n'.format(len(X)))
if args.load_model is None:
model = sklearn.ensemble.RandomForestClassifier(bootstrap=False,
class_weight = args.sklearn_class_weight,
n_jobs = args.sklearn_jobs,
n_estimators = args.sklearn_estimators,
verbose = args.sklearn_verbosity,
min_samples_leaf = args.sklearn_min_samples_leaf,
max_features = args.sklearn_max_features,
max_depth = args.sklearn_max_depth
)
options = (ENABLE_FEATURE_IAT, ENABLE_FEATURE_PKTLEN, ENABLE_FEATURE_DIRS, ENABLE_FEATURE_BINS, args.proto_class)
sys.stderr.write('Training model..\n')
model.fit(X, y)
if args.generate_feature_importance is True:
sys.stderr.write('Generating feature importance .. this may take some time\n')
plotPermutatedImportance(model, X, y)
if args.save_model is not None:
sys.stderr.write('Saving model to {}\n'.format(args.save_model))
joblib.dump([model, options], args.save_model)
print('ENABLE_FEATURE_PKTLEN: {}'.format(ENABLE_FEATURE_PKTLEN))
print('ENABLE_FEATURE_BINS..: {}'.format(ENABLE_FEATURE_BINS))
print('ENABLE_FEATURE_DIRS..: {}'.format(ENABLE_FEATURE_DIRS))
print('ENABLE_FEATURE_IAT...: {}'.format(ENABLE_FEATURE_IAT))
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')
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))
nsock = nDPIsrvdSocket()
nsock.connect(address)
nsock.loop(onJsonLineRecvd, None, (model, args.proto_class, args.disable_colors))