#!/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,))