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MLTD/src/Training.py
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# -*- coding: utf-8 -*- import psycopg2.extras from datetime import datetime, timedelta import traceback import sys import pandas as pd import Utils import logging import logging.config import yaml import os try: import cPickle as pickle except: import pickle import PredictionKorvesis as pdm MYDIR = os.path.dirname(os.path.realpath(__file__)) LOGGING_CONF_FILE = os.path.join(MYDIR, "logging.yml") def read_log_conf(yaml_file): with open(yaml_file) as f: logging.config.dictConfig(yaml.safe_load(f)) read_log_conf(LOGGING_CONF_FILE) logger = logging.getLogger("mltd-offline") def connect_time_db( time_db_host, time_db_port, time_db_username, time_db_password, time_db_ssl, time_db_dbname, ): conn = psycopg2.connect( dbname=time_db_dbname, user=time_db_username, password=time_db_password, host=time_db_host, ) cursor = conn.cursor(cursor_factory=psycopg2.extras.DictCursor) return cursor def fetch_training_data( time_db_cursor, explicit_security_incidents_dates, hours_before, asset_id, adt_table, xlsiem_table, od_table, ): # fetch reported incidents from ADT table time_db_cursor.execute( "SELECT time FROM " + adt_table + " where asset_id = '" + asset_id + "'" ) major_security_incidents_dates = time_db_cursor.fetchall() # append to the fetched incident the explicit incidents if len(explicit_security_incidents_dates) != 0: for date_str in explicit_security_incidents_dates: major_security_incidents_dates.append( [datetime.strptime(date_str, "%Y-%m-%dT%H:%M:%SZ"),"target_incident"] ) major_security_incidents_dates = sorted(major_security_incidents_dates) # removing incidents that are hours_before close to each other remove_candidates = [] for i in range(len(major_security_incidents_dates) - 1, 0, -1): cur_row = major_security_incidents_dates[i] pre_row = major_security_incidents_dates[i - 1] |
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print(cur_row[0]-pre_row[0]) |
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if cur_row[0] <= pre_row[0] + timedelta(hours=Utils.strtime_to_hours(hours_before)): remove_candidates.append(cur_row) if len(remove_candidates) > 0: |
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for date in remove_candidates: major_security_incidents_dates.remove(date) |
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dataset_dates = [] dataset_values = [] for msi_date in major_security_incidents_dates: time_db_cursor.execute( "SELECT time, event_alarm_id FROM " + xlsiem_table + " where time > TIMESTAMP '" + msi_date[0].strftime("%Y-%m-%dT%H:%M:%SZ") + "' - interval '" + str(hours_before) + " hours' and asset_id = '" + asset_id + "'" ) xlsiem_security_incidents = time_db_cursor.fetchall() od_security_incidents = [] # time_db_cursor.execute( # "SELECT time, event_alarm_id FROM " # + od_table # + " where time > TIMESTAMP '" # + msi_date[0].strftime("%Y-%m-%dT%H:%M:%SZ") # + "' - interval '" # + str(hours_before) # + " hours' and asset_id = '" # + asset_id # + "'" # ) # od_security_incidents = time_db_cursor.fetchall() fetched_security_incidents = sorted(xlsiem_security_incidents+od_security_incidents) if len(fetched_security_incidents) > 0: for sec_incident in fetched_security_incidents: dataset_dates.append(sec_incident[0].strftime("%Y-%m-%dT%H:%M:%SZ")) dataset_values.append(sec_incident[1]) dataset_dates.append(msi_date[0].strftime("%Y-%m-%dT%H:%M:%SZ")) |
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dataset_values.append("Exploits_All") |
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return (dataset_dates, dataset_values) def save_to_bin_file(data, filename): with open(filename, "ab") as f: pickle.dump(data, f, protocol=pickle.HIGHEST_PROTOCOL) def do_the_training( trainID, time_db_host, time_db_port, time_db_username, time_db_password, time_db_ssl, time_db_dbname, asset_id, adt_table, xlsiem_table, od_table, mp_thres_X, mp_thres_Y, mp_thres_Z, pat_length, s, midpoint, hours_before, time_segments, explicit_security_incidents_dates, artificial_events_generation=False, rre=False, rfe=False, kofe=False, mil_over=False, fs=False, ): try: logger.info("Training process started") time_db_cursor = connect_time_db( time_db_host, time_db_port, time_db_username, time_db_password, time_db_ssl, time_db_dbname, ) dataset_dates, dataset_values = fetch_training_data( time_db_cursor, explicit_security_incidents_dates, hours_before, asset_id, adt_table, xlsiem_table, od_table, ) |
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target_event_id = "Exploits_All" |
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dataset = pd.DataFrame( {"Timestamps": dataset_dates, "Event_id": dataset_values} ) logger.info("serializing pattern length") save_to_bin_file(pat_length, "train_" + str(trainID) + ".dat") logger.info("serializing weak bins") save_to_bin_file({}, "train_" + str(trainID) + ".dat") logger.info("serializing matrixProfile") save_to_bin_file({}, "train_" + str(trainID) + ".dat") logger.info("serializing measurements") save_to_bin_file(dataset_values, "train_" + str(trainID) + ".dat") regr, feature_importance = pdm.train_rf_model( dataset, time_segments, target_event_id, s, midpoint, rre=rre, rfe=rfe, kofe=kofe, mil_over=mil_over, fs=fs, ) # time_segments='H' logger.info("serializing random forest trained model") save_to_bin_file(regr, "train_" + str(trainID) + ".dat") logger.info("serializing random forest feature importance") save_to_bin_file(feature_importance, "train_" + str(trainID) + ".dat") logger.info("serializing flag artificial_events_generation") save_to_bin_file(artificial_events_generation, "train_" + str(trainID) + ".dat") logger.info("Training process finished") time_db_cursor.close() return 0 except Exception as err: print(err) traceback.print_exc(file=sys.stdout) return 5 # failure_dates = ["2014-05-07T00:01:02Z","2014-07-06T00:01:07Z","2014-10-03T00:01:00Z","2014-12-21T00:00:59Z","2015-02-14T00:00:59Z","2015-04-15T00:01:07Z","2015-07-01T00:00:45Z","2015-08-24T00:00:55Z","2016-04-24T00:00:50Z"] # do_the_training(2,'localhost',8086,'Axoom3','artificial_events',0.5,1,0.5,6,0.7,2,4,failure_dates) |