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GNU GENERAL PUBLIC LICENSE
Version 3, 29 June 2007
Copyright (C) 2007 Free Software Foundation, Inc. <https://fsf.org/>
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How to Apply These Terms to Your New Programs
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Also add information on how to contact you by electronic and paper mail.
If the program does terminal interaction, make it output a short
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This is free software, and you are welcome to redistribute it
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The hypothetical commands `show w' and `show c' should show the appropriate
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For more information on this, and how to apply and follow the GNU GPL, see
<https://www.gnu.org/licenses/>.
The GNU General Public License does not permit incorporating your program
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<https://www.gnu.org/licenses/why-not-lgpl.html>.

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<img src="https://github.com/rabobank-cdc/Blue-ATTACK/wiki/images/logo.png" alt="Blue ATT&CK" width=20% height=20%>
# Blue ATT&CK
#### Mapping your blue team to ATT&CK
To get started with Blue ATT&CK, check out the
[Wiki](https://github.com/rabobank-cdc/Blue-ATTACK/wiki/Getting-started).
Blue ATT&CK will help blue teams in scoring and comparing data source quality, visibility coverage, detection coverage and threat actor behaviours. The Blue ATT&CK framework consists of a Python tool, YAML administration files and [scoring tables](https://github.com/rabobank-cdc/Blue-ATTACK/raw/master/scoring_table.xlsx) for the different aspects.
Blue ATT&CK will help you to:
- Administrate and score the quality of your data sources.
- Get insight on the visibility you have on for example endpoints.
- Map your detection coverage.
- Map threat actor behaviours.
- Compare visibility, detections and threat actor behaviours in order to uncover possible improvements in detection and visibility. This can help you to prioritise your blue teaming efforts.
## Authors and contribution
This project is developed and maintained by [Marcus Bakker](https://github.com/marcusbakker) (Twitter: [@bakker3m](https://twitter.com/bakk3rm)) and [Ruben Bouman](https://github.com/rubinatorz) (Twitter: [@rubenb_2](https://twitter.com/rubenb_2/)). Feel free to contact, DMs are open.
We welcome contributions! Contributions can be both in code, as well as in ideas you might have for further development, usability improvements, etc.
### Work of others
Some functionality within Blue ATT&CK was inspired by work of
others:
- Roberto Rodriguez's work on data quality and scoring of ATT&CK techniques ([How Hot Is Your Hunt Team?](https://cyberwardog.blogspot.com/2017/07/how-hot-is-your-hunt-team.html), [Ready to hunt? First, Show me your data!](https://cyberwardog.blogspot.com/2017/12/ready-to-hunt-first-show-me-your-data.html)).
- The MITRE ATT&CK Mapping project on GitHub:
https://github.com/siriussecurity/mitre-attack-mapping.
## Example
YAML files are used for administrating scores and relevant metadata. All
of which can be visualised by loading JSON layer files into the [ATT&CK Navigator](https://github.com/mitre-attack/attack-navigator) (some types of scores and metadata can also be written to Excel).
See below an example of mapping your data sources to ATT&CK which gives you a rough overview of your visibility coverage:
<img src="https://github.com/rabobank-cdc/Blue-ATTACK/wiki/images/example_data_sources.png" alt="Blue ATT&CK"><br>
## Installation and requirements
See our GitHub Wiki: [Installation and requirements](https://github.com/rabobank-cdc/Blue-ATTACK/wiki/Installation-and-requirements).
## Future developments
- Add more graphs:
- [ ] Detections: improvement based on newly added detections and improvements on the level/score of existing detections. Possibly with a changelog with support for comments.
- [ ] Visibility: improvement in the quality of an existing data source.
- Groups:
- [ ] Have groups YAML file type that contains a count on how popular a certain technique is. This can be very useful to map things such as Red Canary's [Threat Detection Report 2019](https://redcanary.com/resources/guides/threat-detection-report/).
- Excel output for:
- [ ] Techniques administration YAML file: visibility coverage
- [ ] Techniques administration YAML file: detection coverage
- Data quality Excel sheet:
- [ ] Add colors to the data quality scores in the Excel sheet.
- YAML files:
- [ ] Create an option within the tool to migrate an old administration YAML file version to a new version (such as adding specific key-value pairs).
- MITRE ATT&CK updates
- [ ] Have a smart way of knowing what to update in your data source and technique administration files once MITRE releases updates.
- [ ] Data sources: check for missing data sources in data sources administration files.
- Minimal visibility
- [ ] Integrate information into the framework on what a minimal set of visibility for a technique should be, before you can say to have useful visibility (e.g. technique X requires at least to have visibility on process monitoring, process command line monitoring and DLL monitoring.)
## License: GPL-3.0
[Blue ATT&CK's GNU General Public License v3.0](https://github.com/rabobank-cdc/Blue-ATTACK/blob/master/LICENSE)

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import argparse
from interactive_menu import *
import os
import signal
def init_menu():
"""
Initialise the command line parameter menu.
:return:
"""
menu_parser = argparse.ArgumentParser(description='Create MITRE ATT&CK layers for visibility, detection and groups.',
epilog='Source: https://github.com/rabobank-cdc/Blue-ATTACK')
menu_parser.add_argument('--version', action='version', version='%(prog)s ' + VERSION)
menu_parser.add_argument('-i', '--interactive', help='launch the interactive menu, which has support for all modes',
action='store_true')
# add subparsers
subparsers = menu_parser.add_subparsers(title='MODE',
description='Select the mode to use. Every mode has its own arguments and '
'help info displayed using: {visibility, detection, group, '
'generic} --help', metavar='', dest='subparser')
# create the data source parser
parser_data_sources = subparsers.add_parser('datasource', help='data source mapping and quality',
aliases=['ds'],
description='Create a heat map based on data sources, output data '
'sources to Excel or generate a data source improvement '
'graph.')
parser_data_sources.add_argument('-f', '--file', help='path to the data source administration YAML file',
required=True)
parser_data_sources.add_argument('-l', '--layer', help='generate a data source layer for the ATT&CK navigator',
action='store_true')
parser_data_sources.add_argument('-e', '--excel', help='generate an Excel sheet with all data source',
action='store_true')
parser_data_sources.add_argument('-g', '--graph', help='generate a graph with data sources added through time',
action='store_true')
parser_data_sources.add_argument('-y', '--yaml', help='generate a technique administration YAML file with '
'visibility scores based on the number of available data '
'sources',
action='store_true')
# create the visibility parser
parser_visibility = subparsers.add_parser('visibility', aliases=['v'],
help='visibility coverage mapping based on techniques and data sources',
description='Create a heat map based on visibility scores or overlay '
'visibility with detections.')
parser_visibility.add_argument('-ft', '--file-tech', help='path to the technique administration YAML file (used to '
'score the level of visibility)', required=True)
parser_visibility.add_argument('-fd', '--file-ds', help='path to the data source administration YAML file (used to '
'add metadata on the involved data sources)',
required=True)
parser_visibility.add_argument('-l', '--layer', help='generate a visibility layer for the ATT&CK navigator',
action='store_true')
parser_visibility.add_argument('-o', '--overlay', help='generate a visibility layer overlayed with detections for '
'the ATT&CK navigator.', action='store_true')
# create the detection parser
parser_detection = subparsers.add_parser('detection', aliases=['d'],
help='detection coverage mapping based on techniques',
description='Create a heat map based on detection scores, overlay '
'detections with visibility or generate a detection '
'improvement graph.')
parser_detection.add_argument('-ft', '--file-tech', help='path to the technique administration YAML file (used to '
'score the level of visibility)', required=True)
parser_detection.add_argument('-fd', '--file-ds', help='path to the data source administration YAML file (used in '
'the overlay with visibility to add metadata on the '
'involved data sources)')
parser_detection.add_argument('-l', '--layer', help='generate detection layer for the ATT&CK navigator',
action='store_true')
parser_detection.add_argument('-o', '--overlay', help='generate a detection layer overlayed with visibility for '
'the ATT&CK navigator.', action='store_true')
parser_detection.add_argument('-g', '--graph', help='generate a graph with detections added through time',
action='store_true')
# create the group parser
parser_group = subparsers.add_parser('group', aliases=['g'],
description='Create threat actor group heat maps, compare group(s) and '
'compare group(s) with visibility and detection coverage.',
help='threat actor group mapping')
parser_group.add_argument('-g', '--groups', help='specify the groups to include separated using commas. '
'Group can be their ID, name or alias (default is all groups). '
'Other option is to provide a YAML file with a custom group(s) '
'(default = all)',
default='all')
parser_group.add_argument('-o', '--overlay', help='specify what to overlay on the group(s) (provided using the '
'arguments \'-g/--groups\'): group(s), visibility or detection. '
'When overlaying a GROUP: the group can be their ID, name or '
'alias separated using commas. Or provide a file path of a YAML '
'file with a custom group(s). When overlaying DETECTION or '
'VISIBILITY provide a YAML with the technique administration.')
parser_group.add_argument('-t', '--overlay-type', help='specify the type of overlay (default = group)',
choices=['group', 'visibility', 'detection'], default='group')
parser_group.add_argument('--software-group', help='add techniques to the heat map by checking which software is '
'used by group(s), and hence which techniques the software '
'supports (does not influence the scores). If overlay group(s) '
'are provided, only software related to those group(s) are '
'included', action='store_true', default=False)
parser_group.add_argument('-p', '--platform', help='specify the platform (default = Windows)',
choices=['all', 'Linux', 'macOS', 'Windows'], default='Windows')
parser_group.add_argument('-s', '--stage', help='specify the stage (default = attack)',
choices=['attack', 'pre-attack'], default='attack')
# create the generic parser
parser_generic = subparsers.add_parser('generic', description='Generic functions which will output to stdout.',
help='includes: statistics on ATT&CK data source and updates on techniques'
', groups and software', aliases=['ge'])
parser_generic.add_argument('-s', '--statistics', help='get a sorted count on how much techniques are covered by a '
'particular data source', action='store_true')
parser_generic.add_argument('-u', '--updates', help='get a sorted list for when updates were released for '
'techniques, groups or software',
choices=['techniques', 'groups', 'software'])
parser_generic.add_argument('--sort', help='sorting of the output from \'-u/--update\' on modified or creation '
'date (default = modified)', choices=['modified', 'created'],
default='modified')
return menu_parser
def menu(menu_parser):
"""
Parser for the command line parameter menu and calls the appropriate functions.
:param menu_parser: the argparse menu as created with 'init_menu()'
:return:
"""
args = menu_parser.parse_args()
if args.interactive:
interactive_menu()
elif args.subparser in ['datasource', 'ds']:
if check_file_type(args.file, FILE_TYPE_DATA_SOURCE_ADMINISTRATION):
if args.layer:
generate_data_sources_layer(args.file)
if args.excel:
export_data_source_list_to_excel(args.file)
if args.graph:
plot_data_sources_graph(args.file)
if args.yaml:
generate_technique_administration_file(args.file)
elif args.subparser in ['visibility', 'v']:
if check_file_type(args.file_tech, FILE_TYPE_TECHNIQUE_ADMINISTRATION) and \
check_file_type(args.file_ds, FILE_TYPE_DATA_SOURCE_ADMINISTRATION):
if args.layer:
generate_visibility_layer(args.file_tech, args.file_ds, False)
if args.overlay:
generate_visibility_layer(args.file_tech, args.file_ds, True)
elif args.subparser in ['group', 'g']:
generate_group_heat_map(args.groups, args.overlay, args.overlay_type, args.stage, args.platform, args.software_group)
elif args.subparser in ['detection', 'd']:
if args.overlay:
if not args.file_ds:
print('[!] Doing an overlay requires adding the data source administration YAML file (\'--file-ds\')')
quit()
if not check_file_type(args.file_ds, FILE_TYPE_DATA_SOURCE_ADMINISTRATION):
quit()
if check_file_type(args.file_tech, FILE_TYPE_TECHNIQUE_ADMINISTRATION):
if args.layer:
generate_detection_layer(args.file_tech, args.file_ds, False)
if args.overlay and check_file_type(args.file_ds, FILE_TYPE_DATA_SOURCE_ADMINISTRATION):
generate_detection_layer(args.file_tech, args.file_ds, True)
if args.graph:
plot_detection_graph(args.file_tech)
elif args.subparser in ['generic', 'ge']:
if args.statistics:
get_statistics()
elif args.updates:
get_updates(args.updates, args.sort)
def prepare_folders():
"""
Create the folders 'cache' and 'output' if they do not exist.
:return:
"""
if not os.path.exists('cache'):
os.mkdir('cache')
if not os.path.exists('output'):
os.mkdir('output')
def signal_handler(signum, frame):
"""
Function to handles exiting via Ctrl+C.
:param signum:
:param frame:
:return:
"""
sys.exit(0)
if __name__ == '__main__':
signal.signal(signal.SIGINT, signal_handler)
prepare_folders()
menu(init_menu())

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import simplejson
from generic import *
import xlsxwriter
import copy
# Imports for pandas and plotly are because of performance reasons in the function that uses these libraries.
def generate_data_sources_layer(filename):
"""
Generates a generic layer for data sources.
:param filename: the filename of the yaml file containing the data sources administration
:return:
"""
my_data_sources, name, platform, exceptions = _load_data_sources(filename)
# Do the mapping between my data sources and MITRE data sources:
my_techniques = _map_and_colorize_techniques(my_data_sources, exceptions)
layer = get_layer_template_data_sources("Data sources " + name, 'description', 'attack', platform)
layer['techniques'] = my_techniques
json_string = simplejson.dumps(layer).replace('}, ', '},\n')
output_filename = 'output/data_sources_' + normalize_name_to_filename(name) + '.json'
with open(output_filename, 'w') as f:
f.write(json_string)
print("File written: " + output_filename)
def plot_data_sources_graph(filename):
"""
Generates a line graph which shows the improvements on numbers of data sources through time.
:param filename: the filename of the yaml file containing the data sources administration
:return:
"""
my_data_sources, name, platform, exceptions = _load_data_sources(filename)
graph_values = []
for t in my_data_sources.values():
if t['date_connected']:
yyyymm = t['date_connected'].strftime('%Y-%m')
graph_values.append({'date': yyyymm, 'count': 1})
import pandas as pd
df = pd.DataFrame(graph_values).groupby('date', as_index=False)[['count']].sum()
df['cumcount'] = df.ix[::1, 'count'].cumsum()[::1]
output_filename = 'output/graph_data_sources.html'
import plotly
import plotly.graph_objs as go
plotly.offline.plot(
{'data': [go.Scatter(x=df['date'], y=df['cumcount'])],
'layout': go.Layout(title="# of data sources for " + name)},
filename=output_filename, auto_open=False
)
print("File written: " + output_filename)
def export_data_source_list_to_excel(filename):
"""
Makes an overview of all MITRE ATT&CK data sources (via techniques) and lists which data sources are present
in the yaml administration including all properties and data quality score.
:param filename: the filename of the yaml file containing the data sources administration
:return:
"""
my_data_sources, name, platform, exceptions = _load_data_sources(filename, filter_empty_scores=False)
excel_filename = 'output/data_sources.xlsx'
workbook = xlsxwriter.Workbook(excel_filename)
worksheet = workbook.add_worksheet('Data sources')
# Formatting:
format_bold_left = workbook.add_format({'align': 'left', 'bold': True})
format_title = workbook.add_format({'align': 'left', 'bold': True, 'font_size': '14'})
format_center = workbook.add_format({'align': 'center'})
# Title
worksheet.write(0, 0, 'Data sources for ' + name, format_title)
# Header columns
worksheet.write(2, 0, 'Data source name', format_bold_left)
worksheet.write(2, 1, 'Date registered', format_bold_left)
worksheet.write(2, 2, 'Date connected', format_bold_left)
worksheet.write(2, 3, 'Products', format_bold_left)
worksheet.write(2, 4, 'Comment', format_bold_left)
worksheet.write(2, 5, 'Available for data analytics', format_bold_left)
worksheet.write(2, 6, 'DQ: device completeness', format_bold_left)
worksheet.write(2, 7, 'DQ: data field completeness', format_bold_left)
worksheet.write(2, 8, 'DQ: timeliness', format_bold_left)
worksheet.write(2, 9, 'DQ: consistency', format_bold_left)
worksheet.write(2, 10, 'DQ: retention', format_bold_left)
worksheet.write(2, 11, 'DQ: score', format_bold_left)
worksheet.set_column(0, 0, 35)
worksheet.set_column(1, 2, 15)
worksheet.set_column(3, 4, 35)
worksheet.set_column(5, 5, 24)
worksheet.set_column(6, 7, 25)
worksheet.set_column(8, 10, 15)
worksheet.set_column(11, 11, 10)
# Putting the data sources data:
y = 3
for d in get_all_mitre_data_sources():
worksheet.write(y, 0, d)
if d in my_data_sources.keys():
ds = my_data_sources[d]
worksheet.write(y, 1, str(ds['date_registered']).replace('None', ''))
worksheet.write(y, 2, str(ds['date_connected']).replace('None', ''))
worksheet.write(y, 3, ', '.join(ds['products']).replace('None', ''))
worksheet.write(y, 4, str(ds['comment']) if ds['comment'] else '')
worksheet.write(y, 5, str(ds['available_for_data_analytics']))
worksheet.write(y, 6, ds['data_quality']['device_completeness'], format_center)
worksheet.write(y, 7, ds['data_quality']['data_field_completeness'], format_center)
worksheet.write(y, 8, ds['data_quality']['timeliness'], format_center)
worksheet.write(y, 9, ds['data_quality']['consistency'], format_center)
worksheet.write(y, 10, ds['data_quality']['retention'], format_center)
score = 0
score_count = 0
for s in ds['data_quality'].values():
if s != 0:
score_count += 1
score += s
if score > 0:
score = score/score_count
worksheet.write(y, 11, score, format_center)
y += 1
worksheet.autofilter(2, 0, 2, 11)
worksheet.freeze_panes(3, 0)
try:
workbook.close()
print("File written: " + excel_filename)
except Exception as e:
print('[!] Error while writing Excel file: %s' % str(e))
def _load_data_sources(filename, filter_empty_scores=True):
"""
Loads the data sources (including all properties) from the given yaml file.
:param filename: the filename of the yaml file containing the data sources administration
:return: dictionaty with data sources, name, platform and exceptions list.
"""
my_data_sources = {}
with open(filename, 'r') as yaml_file:
yaml_content = yaml.load(yaml_file, Loader=yaml.FullLoader)
for d in yaml_content['data_sources']:
dq = d['data_quality']
if not filter_empty_scores:
my_data_sources[d['data_source_name']] = d
elif dq['device_completeness'] > 0 and dq['data_field_completeness'] > 0 and dq['timeliness'] > 0 and dq['consistency'] > 0:
my_data_sources[d['data_source_name']] = d
name = yaml_content['name']
platform = yaml_content['platform']
exceptions = [t['technique_id'] for t in yaml_content['exceptions']]
return my_data_sources, name, platform, exceptions
def _map_and_colorize_techniques(my_ds, exceptions):
"""
Determine the color of the techniques based on how many data sources are available per technique.
:param my_ds: the configured data sources
:return: a dictionary with techniques that can be used in the layer's output file
"""
techniques = load_attack_data(DATATYPE_ALL_TECH)
technique_colors = {}
# Color the techniques based on how many data sources are available.
for t in techniques:
if t['data_sources']:
total_ds_count = len(t['data_sources'])
ds_count = 0
for ds in t['data_sources']:
if ds in my_ds.keys():
ds_count += 1
if total_ds_count > 0:
result = (float(ds_count) / float(total_ds_count)) * 100
color = COLOR_DS_25p if result <= 25 else COLOR_DS_50p if result <= 50 else COLOR_DS_75p \
if result <= 75 else COLOR_DS_99p if result <= 99 else COLOR_DS_100p
technique_colors[t['technique_id']] = color
my_techniques = map_techniques_to_data_sources(techniques, my_ds)
output_techniques = []
for t, v in my_techniques.items():
if t not in exceptions:
for tactic in v['tactics']:
d = {}
d['techniqueID'] = t
# d['score'] = 50
d['color'] = technique_colors[t]
d['comment'] = ''
d['enabled'] = True
d['tactic'] = tactic.lower().replace(' ', '-')
d['metadata'] = [{'name': '-Available data sources', 'value': ', '.join(v['my_data_sources'])},
{'name': '-ATT&CK data sources', 'value': ', '.join(v['data_sources'])},
{'name': '-Products', 'value': ', '.join(v['products'])}]
output_techniques.append(d)
return output_techniques
def generate_technique_administration_file(filename):
"""
Generate a technique administration file based on the data source administration yaml file
:param filename: the filename of the yaml file containing the data sources administration
:return:
"""
my_data_sources, name, platform, exceptions = _load_data_sources(filename)
techniques = load_attack_data(DATATYPE_ALL_TECH)
# This is part of the techniques administration YAML file and is used as a template
dict_tech = {'technique_id': '', 'detection': {'date_registered': None, 'date_implemented': None, 'score': -1,
'location': [''], 'comment': ''},
'visibility': {'score': 0, 'comment': ''}}
yaml_file = {}
yaml_file['version'] = 1.0
yaml_file['file_type'] = FILE_TYPE_TECHNIQUE_ADMINISTRATION
yaml_file['name'] = name
yaml_file['platform'] = platform
yaml_file['techniques'] = []
# Score visibility based on the number of available data sources and the exceptions
for t in techniques:
if t['matrix'] == 'mitre-attack':
platforms_lower = list(map(lambda x: x.lower(), t['platform']))
if platform in platforms_lower:
if t['data_sources']:
total_ds_count = len(t['data_sources'])
ds_count = 0
for ds in t['data_sources']:
if ds in my_data_sources.keys():
ds_count += 1
if total_ds_count > 0:
result = (float(ds_count) / float(total_ds_count)) * 100
score = 0 if result == 0 else 1 if result <= 49 else 2 if result <= 74 else 3 if result <= 99 else 4
else:
score = 0
# Do not add technique if score == 0 or part of the exception list\
techniques_upper = list(map(lambda x: x.upper(), exceptions))
if score > 0 and t['technique_id'] not in techniques_upper:
tech = copy.deepcopy(dict_tech)
tech['technique_id'] = t['technique_id']
tech['visibility']['score'] = score
yaml_file['techniques'].append(tech)
yaml_string = '%YAML 1.2\n---\n' + yaml.dump(yaml_file, sort_keys=False).replace('null', '')
output_filename = 'output/techniques-administration-' + normalize_name_to_filename(name+'-'+platform) + '.yaml'
suffix = 1
while os.path.exists(output_filename):
output_filename = 'output/techniques-administration-' + normalize_name_to_filename(name + '-' + platform) + \
'_' + str(suffix) + '.yaml'
suffix += 1
with open(output_filename, 'w') as f:
f.write(yaml_string)
print("File written: " + output_filename)

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import os
import pickle
from datetime import datetime as dt
import yaml
# Import for attackcti is because of performance reasons in the function that uses this library.
APP_NAME = 'Blue ATT&CK'
APP_DESC = 'Mapping your blue team to ATT&CK'
VERSION = '1.0'
EXPIRE_TIME = 60*60*24
DATATYPE_TECH_BY_GROUP = 'mitre_techniques_used_by_group'
DATATYPE_ALL_TECH = 'mitre_all_techniques'
DATATYPE_ALL_GROUPS = 'mitre_all_groups'
DATATYPE_ALL_SOFTWARE = 'mitre_all_software'
DATATYPE_TECH_BY_SOFTWARE = 'mitre_techniques_used_by_software'
DATATYPE_SOFTWARE_BY_GROUP = 'mitre_software_used_by_group'
# Group colors
COLOR_GROUP_OVERLAY_MATCH = '#f9a825' # orange
COLOR_GROUP_OVERLAY_NO_MATCH = '#ffee58' # yellow
COLOR_SOFTWARE = '#0d47a1 ' # dark blue
COLOR_GROUP_AND_SOFTWARE = '#64b5f6 ' # light blue
COLOR_GRADIENT_MIN = '#ffcece' # light red
COLOR_GRADIENT_MAX = '#ff0000' # red
COLOR_TACTIC_ROW_BACKGRND = '#dddddd' # light grey
COLOR_GROUP_OVERLAY_ONLY_DETECTION = '#8BC34A' # green
COLOR_GROUP_OVERLAY_ONLY_VISIBILITY = '#1976D2' # blue
# data source colors (purple range)
COLOR_DS_25p = '#E1BEE7'
COLOR_DS_50p = '#CE93D8'
COLOR_DS_75p = '#AB47BC'
COLOR_DS_99p = '#7B1FA2'
COLOR_DS_100p = '#4A148C'
# data source colors HAPPY (green range)
COLOR_DS_25p_HAPPY = '#DCEDC8'
COLOR_DS_50p_HAPPY = '#AED581'
COLOR_DS_75p_HAPPY = '#8BC34A'
COLOR_DS_99p_HAPPY = '#689F38'
COLOR_DS_100p_HAPPY = '#33691E'
# Detection colors (green range)
COLOR_D_0 = '#64B5F6' # Blue: Forensics/Context
COLOR_D_1 = '#DCEDC8'
COLOR_D_2 = '#AED581'
COLOR_D_3 = '#8BC34A'
COLOR_D_4 = '#689F38'
COLOR_D_5 = '#33691E'
# Visibility colors (blue range)
COLOR_V_1 = '#BBDEFB'
COLOR_V_2 = '#64B5F6'
COLOR_V_3 = '#1976D2'
COLOR_V_4 = '#0D47A1'
# Detection and visibility overlay color:
COLOR_OVERLAY_VISIBILITY = COLOR_V_3
COLOR_OVERLAY_DETECTION = COLOR_D_3
COLOR_OVERLAY_BOTH = COLOR_GROUP_OVERLAY_MATCH
FILE_TYPE_DATA_SOURCE_ADMINISTRATION = 'data-source-administration'
FILE_TYPE_TECHNIQUE_ADMINISTRATION = 'technique-administration'
FILE_TYPE_GROUP_ADMINISTRATION = 'group-administration'
def save_attack_data(data, path):
"""
Save ATT&CK data to disk for the purpose of caching.
:param data: the MITRE ATT&CK data to save
:param path: file path to write to, including filename
:return:
"""
if not os.path.exists('cache/'):
os.mkdir('cache/')
with open(path, 'wb') as f:
pickle.dump([data, dt.now()], f)
def load_attack_data(data_type):
"""
Load the cached ATT&CK data from disk, if not expired (data file on disk is older then EXPIRE_TIME seconds).
:param data_type: the desired data type, see DATATYPE_XX constants.
:return: MITRE ATT&CK data object
"""
if os.path.exists("cache/" + data_type):
with open("cache/" + data_type, 'rb') as f:
cached = pickle.load(f)
write_time = cached[1]
if not (dt.now() - write_time).total_seconds() >= EXPIRE_TIME:
return cached[0]
from attackcti import attack_client
mitre = attack_client()
json_data = None
if data_type == DATATYPE_TECH_BY_GROUP:
json_data = mitre.get_techniques_used_by_group()
elif data_type == DATATYPE_ALL_TECH:
json_data = mitre.get_all_techniques()
elif data_type == DATATYPE_ALL_GROUPS:
json_data = mitre.get_all_groups()
elif data_type == DATATYPE_ALL_SOFTWARE:
json_data = mitre.get_all_software()
elif data_type == DATATYPE_TECH_BY_SOFTWARE:
json_data = mitre.get_techniques_used_by_software()
elif data_type == DATATYPE_SOFTWARE_BY_GROUP:
json_data = mitre.get_software_used_by_group()
save_attack_data(json_data, "cache/" + data_type)
return json_data
def _get_base_template(name, description, stage, platform, sorting):
"""
Prepares a base template for the json layer file that can be loaded into the MITRE ATT&CK Navigator.
More information on the version 2.1 layer format:
https://github.com/mitre/attack-navigator/blob/master/layers/LAYERFORMATv2_1.md
:param name: name
:param description: description
:param stage: stage (act | prepare)
:param platform: platform
:param sorting: sorting
:return: layer template dictionary
"""
layer = {}
layer['name'] = name
layer['version'] = '2.1'
layer['domain'] = 'mitre-enterprise'
layer['description'] = description
if platform == 'all':
platform = ['windows', 'linux', 'mac']
else:
platform = [platform.lower()]
if stage == 'attack':
layer['filters'] = {'stages': ['act'], 'platforms': platform}
else:
layer['filters'] = {'stages': ['prepare'], 'platforms': platform}
layer['sorting'] = sorting
layer['viewMode'] = 0
layer['hideDisable'] = False
layer['techniques'] = []
layer['showTacticRowBackground'] = False
layer['tacticRowBackground'] = COLOR_TACTIC_ROW_BACKGRND
layer['selectTechniquesAcrossTactics'] = True
return layer
def get_layer_template_groups(name, max_score, description, stage, platform):
"""
Prepares a base template for the json layer file that can be loaded into the MITRE ATT&CK Navigator.
More information on the version 2.1 layer format:
https://github.com/mitre/attack-navigator/blob/master/layers/LAYERFORMATv2_1.md
:param name: name
:param max_score: max_score
:param description: description
:param stage: stage (act | prepare)
:param platform: platform
:return: layer template dictionary
"""
layer = _get_base_template(name, description, stage, platform, 3)
layer['gradient'] = {'colors': [COLOR_GRADIENT_MIN, COLOR_GRADIENT_MAX], 'minValue': 0, 'maxValue': max_score}
layer['legendItems'] = \
[
{'label': 'Tech. ref. for ' + str(1) + ' group', 'color': COLOR_GRADIENT_MIN},
{'label': 'Tech. ref. for ' + str(max_score) + ' groups', 'color': COLOR_GRADIENT_MAX},
{'label': 'Groups overlay: tech. in group + overlay', 'color': COLOR_GROUP_OVERLAY_MATCH},
{'label': 'Groups overlay: tech. in overlay', 'color': COLOR_GROUP_OVERLAY_NO_MATCH},
{'label': 'Src. of tech. is only software', 'color': COLOR_SOFTWARE},
{'label': 'Src. of tech. is group(s)/overlay + software', 'color': COLOR_GROUP_AND_SOFTWARE}
]
return layer
def get_layer_template_detections(name, description, stage, platform):
"""
Prepares a base template for the json layer file that can be loaded into the MITRE ATT&CK Navigator.
More information on the version 2.1 layer format:
https://github.com/mitre/attack-navigator/blob/master/layers/LAYERFORMATv2_1.md
:param name: name
:param description: description
:param stage: stage (act | prepare)
:param platform: platform
:return: layer template dictionary
"""
layer = _get_base_template(name, description, stage, platform, 0)
layer['gradient'] = {'colors': ['#ff6666', '#ffe766', '#8ec843'], 'minValue': 0, 'maxValue': 100}
layer['legendItems'] = \
[
{'label': 'Detection score 0: Forensics/Context', 'color': COLOR_D_0},
{'label': 'Detection score 1: Basic', 'color': COLOR_D_1},
{'label': 'Detection score 2: Fair', 'color': COLOR_D_2},
{'label': 'Detection score 3: Good', 'color': COLOR_D_3},
{'label': 'Detection score 4: Very good', 'color': COLOR_D_4},
{'label': 'Detection score 5: Excellent', 'color': COLOR_D_5}
]
return layer
def get_layer_template_data_sources(name, description, stage, platform):
"""
Prepares a base template for the json layer file that can be loaded into the MITRE ATT&CK Navigator.
More information on the version 2.1 layer format:
https://github.com/mitre/attack-navigator/blob/master/layers/LAYERFORMATv2_1.md
:param name: name
:param description: description
:param stage: stage (act | prepare)
:param platform: platform
:return: layer template dictionary
"""
layer = _get_base_template(name, description, stage, platform, 0)
layer['gradient'] = {'colors': ['#ff6666', '#ffe766', '#8ec843'], 'minValue': 0, 'maxValue': 100}
layer['legendItems'] = \
[
{'label': '1-25% of data sources available', 'color': COLOR_DS_25p},
{'label': '26-50% of data sources available', 'color': COLOR_DS_50p},
{'label': '51-75% of data sources available', 'color': COLOR_DS_75p},
{'label': '76-99% of data sources available', 'color': COLOR_DS_99p},
{'label': '100% of data sources available', 'color': COLOR_DS_100p}
]
return layer
def get_layer_template_visibility(name, description, stage, platform):
"""
Prepares a base template for the json layer file that can be loaded into the MITRE ATT&CK Navigator.
More information on the version 2.1 layer format:
https://github.com/mitre/attack-navigator/blob/master/layers/LAYERFORMATv2_1.md
:param name: name
:param description: description
:param stage: stage (act | prepare)
:param platform: platform
:return: layer template dictionary
"""
layer = _get_base_template(name, description, stage, platform, 0)
layer['gradient'] = {'colors': ['#ff6666', '#ffe766', '#8ec843'], 'minValue': 0, 'maxValue': 100}
layer['legendItems'] = \
[
{'label': 'Visibility score 1: Minimal', 'color': COLOR_V_1},
{'label': 'Visibility score 2: Medium', 'color': COLOR_V_2},
{'label': 'Visibility score 3: Good', 'color': COLOR_V_3},
{'label': 'Visibility score 4: Excellent', 'color': COLOR_V_4}
]
return layer
def get_layer_template_layered(name, description, stage, platform):
"""
Prepares a base template for the json layer file that can be loaded into the MITRE ATT&CK Navigator.
More information on the version 2.1 layer format:
https://github.com/mitre/attack-navigator/blob/master/layers/LAYERFORMATv2_1.md
:param name: name
:param description: description
:param stage: stage (act | prepare)
:param platform: platform
:return: layer template dictionary
"""
layer = _get_base_template(name, description, stage, platform, 0)
layer['gradient'] = {'colors': ['#ff6666', '#ffe766', '#8ec843'], 'minValue': 0, 'maxValue': 100}
layer['legendItems'] = \
[
{'label': 'Visibility', 'color': COLOR_OVERLAY_VISIBILITY},
{'label': 'Detection', 'color': COLOR_OVERLAY_DETECTION},
{'label': 'Visibility and detection', 'color': COLOR_OVERLAY_BOTH}
]
return layer
def get_technique(techniques, technique_id):
"""
Generic function to lookup a specific technique_id in a list of dictionaries with techniques.
:param techniques: list with all techniques
:param technique_id: technique_id to look for
:return: the technique you're searching for. None if not found.
"""
for t in techniques:
if technique_id == t['technique_id']:
return t
return None
def normalize_name_to_filename(name):
"""
Normalize the input filename to a lowercase filename and replace spaces with dashes.
:param name: input filename
:return: normalized filename
"""
return name.lower().replace(' ', '-')
def map_techniques_to_data_sources(techniques, my_data_sources):
"""
This function maps the MITRE ATT&CK techniques to your data sources.
:param techniques: list with all MITRE ATT&CK techniques
:param my_data_sources: your configured data sources
:return: a dictionary containing techniques that can be used in the layer output file.
"""
my_techniques = {}
for i_ds in my_data_sources.keys():
# Loop through all techniques, to find techniques using that data source:
for t in techniques:
# If your data source is in the list of data sources for this technique AND if the
# technique isn't added yet (by an other data source):
if t['data_sources'] and i_ds in t['data_sources'] and t['technique_id'] not in my_techniques.keys():
my_techniques[t['technique_id']] = {}
my_techniques[t['technique_id']]['my_data_sources'] = [i_ds, ]
my_techniques[t['technique_id']]['data_sources'] = t['data_sources']
my_techniques[t['technique_id']]['tactics'] = t['tactic']
my_techniques[t['technique_id']]['products'] = my_data_sources[i_ds]['products']
elif t['data_sources'] and i_ds in t['data_sources'] and t['technique_id'] in my_techniques.keys():
my_techniques[t['technique_id']]['my_data_sources'].append(i_ds)
return my_techniques
def get_all_mitre_data_sources():
"""
Gets all the data sources from the techniques and make a unique sorted list of it.
:return: a sorted list with all data sources
"""
techniques = load_attack_data(DATATYPE_ALL_TECH)
data_sources = set()
for t in techniques:
if t['data_sources']:
for ds in t['data_sources']:
if ds not in data_sources:
data_sources.add(ds)
return sorted(data_sources)
def check_file_type(filename, file_type=None):
"""
Check if the provided YAML file has the key 'file_type' and possible if that key matches a specific value.
:param filename: path to a YAML file
:param file_type: value to check against the 'file_type' key in the YAML file
:return: the file_type if present, else None is returned.
"""
if not os.path.exists(filename):
print('[!] File: \'' + filename + '\' does not exist')
return None
with open(filename, 'r') as yaml_file:
try:
yaml_content = yaml.load(yaml_file, Loader=yaml.FullLoader)
except:
print('[!] File: \'' + filename + '\' is not a valid YAML file.')
return None
if not hasattr(yaml_content, 'keys'):
print('[!] File: \'' + filename + '\' is not a valid YAML file.')
return None
if 'file_type' not in yaml_content.keys():
print('[!] File: \'' + filename + '\' does not contain a file_type key.')
return None
elif file_type:
if file_type != yaml_content['file_type']:
print('[!] File: \'' + filename + '\' is not a file type of: \'' + file_type + '\'')
return None
else:
return yaml_content['file_type']
else:
return yaml_content['file_type']

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import simplejson
from generic import *
from technique_mapping import _load_detections
CG_GROUPS = {}
def is_in_group(json_groups, argument_groups):
"""
Check if the two dicts (json_groups and argument_groups) have any groups in common based on their names/aliases.
:param json_groups: group aliases from ATT&CK
:param argument_groups: group names provided via the command line by the user
:return: true or false
"""
json_groups = list(map(lambda x: x.lower(), json_groups))
for group in argument_groups:
if group in json_groups:
return True
return False
def is_group_found(groups_found, argument_groups):
"""
Check if a group that has been provided using '-g/--groups'/'-o/--overlay' is present within MITRE ATT&CK.
:param groups_found: groups that are found in the ATT&CK data
:param argument_groups: groups provided via the command line by the user
:return: returns boolean that incidates if the group is found
"""
groups_json = load_attack_data(DATATYPE_ALL_GROUPS)
for group_arg in argument_groups:
if group_arg == 'all': # this one will be ignored as it does not make any sense for this function
return True
group_id = None
for group in groups_json: # is the group provided via the command line known in ATT&CK?
if group['group_aliases']:
group_aliases_lower = list(map(lambda x: x.lower(), group['group_aliases']))
if group_arg in group_aliases_lower or group_arg == group['group_id'].lower():
group_id = group['group_id']
if not group_id: # the group that has been provided through the command line cannot be found in ATT&CK
print('[!] Unknown group: ' + group_arg)
return False
elif group_id not in groups_found: # group not present in filtered data sate (i.e. platform and stage)
print('[!] Group not part of the data set: ' + group_arg)
return False
else:
return True
def get_software_techniques(groups, stage, platform):
"""
Get all techniques (in a dict) from the provided list of groups in relation to the software these groups use,
and hence techniques they support.
:param groups: ATT&CK groups
:param stage: attack or pre-attack
:param platform: the applicable platform
:return: dictionary with info on groups
"""
# { group_id: {group_name: NAME, techniques: set{id, ...} } }
groups_dict = {}
tech_by_software_json = load_attack_data(DATATYPE_TECH_BY_SOFTWARE)
# { software_id: [technique, ...] }
software_dict = {}
for tech in tech_by_software_json:
if tech['software_id'] not in software_dict:
software_dict[tech['software_id']] = set([tech['technique_id']])
else:
software_dict[tech['software_id']].add(tech['technique_id'])
# groups is a YAML file
if os.path.isfile(str(groups)):
with open(groups, 'r') as yaml_file:
config = yaml.load(yaml_file, Loader=yaml.FullLoader)
for group in config['groups']:
if group['enabled']:
group_id = get_group_id(group['group_name'], group['campaign'])
groups_dict[group_id] = dict()
groups_dict[group_id]['group_name'] = group['group_name']
groups_dict[group_id]['techniques'] = set()
groups_dict[group_id]['campaign'] = group['campaign']
groups_dict[group_id]['software'] = group['software_id']
if group['software_id']:
for soft_id in group['software_id']:
try:
groups_dict[group_id]['techniques'].update(software_dict[soft_id])
except KeyError:
print('[!] unknown ATT&CK software ID: ' + soft_id)
# groups are provided as arguments via the command line
else:
software_by_group_json = load_attack_data(DATATYPE_SOFTWARE_BY_GROUP)
for s in software_by_group_json:
# group matches the: matrix/stage, platform and the group(s) we are interested in
if s['software_platform']: # their is some software that do not have a platform, skip those
if s['matrix'] == 'mitre-'+stage and (platform in s['software_platform'] or platform == 'all') and \
(groups[0] == 'all' or s['group_id'].lower() in groups or is_in_group(s['group_aliases'], groups)):
if s['group_id'] not in groups_dict:
groups_dict[s['group_id']] = {'group_name': s['group']}
groups_dict[s['group_id']]['techniques'] = set()
groups_dict[s['group_id']]['techniques'].update(software_dict[s['software_id']])
return groups_dict
def get_group_id(group_name, campaign):
# CG_GROUPS = { group_name+campaign: id } }
"""
Generate a custom group id.
:param group_name: group name as used within the YAML file
:param campaign: campaign as used within the YAML file
:return: custom group identifier string (e.g. CG0001)
"""
global CG_GROUPS
if not CG_GROUPS:
new_id = 1
elif group_name + campaign not in CG_GROUPS:
new_id = len(CG_GROUPS) + 1
if group_name + campaign not in CG_GROUPS:
length = len(str(new_id))
if length > 9:
cg_id = 'CG00' + str(new_id)
elif length > 99:
cg_id = 'CG0' + str(new_id)
elif length > 999:
cg_id = 'CG' + str(new_id)
else:
cg_id = 'CG000' + str(new_id)
CG_GROUPS[group_name + campaign] = cg_id
return CG_GROUPS[group_name + campaign]
def get_group_techniques(groups, stage, platform, file_type):
"""
Get all techniques (in a dict) from the provided list of groups
:param groups: group ID, group name/alias or a YAML file with group(s) data
:param stage: attack or pre-attack
:param platform: all, Linux, macOS, Windows
:param file_type: the file type of the YAML file as present in the key 'file_type'
:return: returns dictionary with all techniques from the provided list of groups or -1 when group is not found
"""
# { group_id: {group_name: NAME, techniques: set{id, ...} } }
groups_dict = {}
groups_found = set()
# groups is a YAML file
if file_type == FILE_TYPE_GROUP_ADMINISTRATION:
with open(groups, 'r') as yaml_file:
config = yaml.load(yaml_file, Loader=yaml.FullLoader)
for group in config['groups']:
if group['enabled']:
group_id = get_group_id(group['group_name'], group['campaign'])
groups_dict[group_id] = dict()
groups_dict[group_id]['group_name'] = group['group_name']
groups_dict[group_id]['techniques'] = set(group['technique_id'])
groups_dict[group_id]['campaign'] = group['campaign']
groups_dict[group_id]['software'] = group['software_id']
else:
# groups are provided as arguments via the command line
groups_json = load_attack_data(DATATYPE_TECH_BY_GROUP)
for e in groups_json:
json_platform = e['platform']
if not json_platform:
# we just set this to an random legit value, because for pre-attack 'platform' is not used
json_platform = 'Windows'
# group matches the: matrix/stage, platform and the group(s) we are interested in
if e['matrix'] == 'mitre-'+stage and (platform in json_platform or platform == 'all') and \
(groups[0] == 'all' or e['group_id'].lower() in groups or is_in_group(e['group_aliases'], groups)):
if e['group_id'] not in groups_dict:
groups_found.add(e['group_id'])
groups_dict[e['group_id']] = {'group_name': e['group']}
groups_dict[e['group_id']]['techniques'] = set()
groups_dict[e['group_id']]['techniques'].add(e['technique_id'])
# do not call 'is_group_found' when groups is a YAML file
# (this could contain groups that do not exists within ATT&CK)
if not os.path.isfile(str(groups)):
found = is_group_found(groups_found, groups)
if not found:
return -1
return groups_dict
def get_detection_techniques(filename):
"""
Get all techniques (in a dict) from the detection administration
:param filename: path to the YAML technique administration file
:return: dictionary
"""
# { group_id: {group_name: NAME, techniques: set{id, ...} } }
groups_dict = {}
detection_techniques, name, platform = _load_detections(filename)
group_id = 'DETECTION'
groups_dict[group_id] = {}
groups_dict[group_id]['group_name'] = 'Detection'
groups_dict[group_id]['techniques'] = set()
for t, v in detection_techniques.items():
if 'detection' in v.keys() and v['detection']['score'] > 0:
groups_dict[group_id]['techniques'].add(t)
return groups_dict
def get_visibility_techniques(filename):
"""
Get all techniques (in a dict) from the detections administration
:param filename: path to the YAML technique administration file
:return: dictionary
"""
# { group_id: {group_name: NAME, techniques: set{id, ...} } }
groups_dict = {}
detection_techniques, name, platform = _load_detections(filename)
group_id = 'VISIBILITY'
groups_dict[group_id] = {}
groups_dict[group_id]['group_name'] = 'Visibility'
groups_dict[group_id]['techniques'] = set()
for t, v in detection_techniques.items():
if 'visibility' in v.keys() and v['visibility']['score'] > 0:
groups_dict[group_id]['techniques'].add(t)
return groups_dict
def get_technique_count(groups, groups_overlay, groups_software):
"""
Create a dict with all involved techniques and their relevant count/score
:param groups: a dict with data on groups
:param groups_overlay: a dict with data on the groups to overlay
:param groups_software: a dict with with data on which techniques are used within related software
:return: dictionary
"""
# { technique_id: {count: ..., groups: set{} }
techniques_dict = {}
for group, v in groups.items():
for tech in v['techniques']:
if tech not in techniques_dict:
techniques_dict[tech] = dict()
techniques_dict[tech]['groups'] = set()
techniques_dict[tech]['count'] = 1
else:
techniques_dict[tech]['count'] += 1
techniques_dict[tech]['groups'].add(group)
for group, v in groups_overlay.items():
for tech in v['techniques']:
if tech not in techniques_dict:
techniques_dict[tech] = dict()
techniques_dict[tech]['groups'] = set()
techniques_dict[tech]['count'] = 1
elif group in groups:
if tech not in groups[group]['techniques']:
techniques_dict[tech]['count'] += 1
# Only to this when it was not already counted by being part of 'groups'.
# Meaning the group in 'groups_overlay' was also part of 'groups' (match on Group ID) and the
# technique was already counted for that group / it is not a new technique for that group coming
# from a YAML file
else:
techniques_dict[tech]['count'] += 1
# increase count when the group in the YAML file is a custom group
techniques_dict[tech]['groups'].add(group)
for group, v in groups_software.items():
for tech in v['techniques']:
if tech not in techniques_dict:
techniques_dict[tech] = dict()
techniques_dict[tech]['count'] = 0
# we will not adjust the scoring for groups_software. We will just set the the score to 0.
# This will later be used for the colouring of the heat map.
if 'groups' not in techniques_dict[tech]:
techniques_dict[tech]['groups'] = set()
techniques_dict[tech]['groups'].add(group)
return techniques_dict
def get_technique_layer(techniques, groups, overlay, groups_software, overlay_file_type, overlay_type):
"""
Create the technique layer that will be part of the ATT&CK navigator json file
:param techniques: involved techniques with count (to be used within the scores)
:param groups: a dict with data on groups
:param overlay: a dict with data on the groups to overlay
:param groups_software: a dict with with data on which techniques are used within related software
:param overlay_file_type: the file type of the YAML file as present in the key 'file_type'
:param overlay_type: group, visibility or detection
:return: dictionary
"""
techniques_layer = []
# { technique_id: {count: ..., groups: set{} }
# add the technique count/scoring
for tech, v in techniques.items():
t = dict()
t['techniqueID'] = tech
t['score'] = v['count']
t['metadata'] = []
metadata_dict = dict()
for group, values in groups.items():
if tech in values['techniques']: # we do not color this one because that's done using the scoring
if 'Groups' not in metadata_dict:
metadata_dict['Groups'] = set()
metadata_dict['Groups'].add(values['group_name'])
# this will only be effective when loading a YAML files that has a value for the key 'campaign'
if 'Campaign' in values and values['campaign'] is not None:
if 'CAMPAIGN' not in metadata_dict:
metadata_dict['Campaign'] = set()
metadata_dict['Campaign'].add(values['campaign'])
# change the color and add metadata to make the groups overlay visible
for group, values in overlay.items():
if tech in values['techniques']:
if len(v['groups'].intersection(set(groups.keys()))) > 0:
# if the technique is both present in the group (-g/--groups) and the groups overlay (-o/--overlay)
t['color'] = COLOR_GROUP_OVERLAY_MATCH
else:
# the technique is only present in the overlay and not in the provided groups (-g/--groups)
if overlay_file_type == FILE_TYPE_TECHNIQUE_ADMINISTRATION:
if overlay_type == 'visibility':
t['color'] = COLOR_GROUP_OVERLAY_ONLY_VISIBILITY
elif overlay_type == 'detection':
t['color'] = COLOR_GROUP_OVERLAY_ONLY_DETECTION
else:
t['color'] = COLOR_GROUP_OVERLAY_NO_MATCH
if 'Overlay' not in metadata_dict:
metadata_dict['Overlay'] = set()
metadata_dict['Overlay'].add(values['group_name'])
# this will only be effective when loading a YAML files that has a value for the key 'campaign'
if 'campaign' in values and values['campaign'] is not None:
if 'Campaign' not in metadata_dict:
metadata_dict['Campaign'] = set()
metadata_dict['Campaign'].add(values['campaign'])
# change the color and add metadata to make the groups software overlay visible
for group, values in groups_software.items(): # TODO add support for campaign info in layer metadata
if tech in values['techniques']:
if t['score'] > 0:
t['color'] = COLOR_GROUP_AND_SOFTWARE
else:
t['color'] = COLOR_SOFTWARE
if 'Software groups' not in metadata_dict:
metadata_dict['Software groups'] = set()
metadata_dict['Software groups'].add(values['group_name'])
# create the metadata based on the dict 'metadata_dict'
for metadata, values in metadata_dict.items():
tmp_dict = {'name': '-' + metadata, 'value': ', '.join(values)}
t['metadata'].append(tmp_dict)
techniques_layer.append(t)
return techniques_layer
def get_group_list(groups, file_type):
"""
Make a list of group names for the involved groups.
:param groups: a dict with data on groups
:param file_type: the file type of the YAML file as present in the key 'file_type'
:return: list
"""
if file_type == FILE_TYPE_GROUP_ADMINISTRATION:
groups_list = []
for group, values in groups.items():
# if YAML file contains campaign key with a legit value
if 'campaign' in values and values['campaign'] is not None:
groups_list.append(values['group_name'] + ' (' + values['campaign'] + ')')
else:
groups_list.append(values['group_name'])
return groups_list
else:
return groups
def generate_group_heat_map(groups, overlay, overlay_type, stage, platform, software_groups):
"""
Calls all functions that are necessary for the generation of the heat map and write a json layer to disk.
:param groups: threat actor groups
:param overlay: group(s), visibility or detections to overlay (group ID, group name/alias, YAML file with
group(s), detections or visibility)
:param overlay_type: group, visibility or detection
:param stage: attack or pre-attack
:param platform: all, Linux, macOS, Windows
:param software_groups: specify if techniques from related software should be included.
:return: returns nothing when something's wrong
"""
overlay_dict = {}
groups_software_dict = {}
groups_file_type = None
if os.path.isfile(groups):
groups_file_type = check_file_type(groups, file_type=FILE_TYPE_GROUP_ADMINISTRATION)
if not groups_file_type:
return
else:
# remove whitespaces (leading and trailing), convert to lower case and put in a list
groups = groups.split(',')
groups = list(map(lambda x: x.strip().lower(), groups))
overlay_file_type = None
if overlay:
if os.path.isfile(overlay):
expected_file_type = FILE_TYPE_GROUP_ADMINISTRATION if overlay_type == 'group' else FILE_TYPE_TECHNIQUE_ADMINISTRATION if overlay_type in ['visibility', 'detection'] else None
overlay_file_type = check_file_type(overlay, expected_file_type)
if not overlay_file_type:
return
else:
overlay = overlay.split(',')
overlay = list(map(lambda x: x.strip().lower(), overlay))
else:
overlay = []
if overlay_file_type == FILE_TYPE_TECHNIQUE_ADMINISTRATION:
if overlay_type == 'visibility':
overlay_dict = get_visibility_techniques(overlay)
elif overlay_type == 'detection':
overlay_dict = get_detection_techniques(overlay)
elif len(overlay) > 0:
overlay_dict = get_group_techniques(overlay, stage, platform, overlay_file_type)
if not overlay_dict:
return
groups_dict = get_group_techniques(groups, stage, platform, groups_file_type)
if groups_dict == -1:
return
if len(groups_dict) == 0:
print('[!] Empty layer.') # the provided groups dit not result in any techniques
return
# check if we are doing an software group overlay
if software_groups and overlay: # TODO add support for campaign info in layer metadata
# if a group overlay is provided, get the software techniques for the overlay
groups_software_dict = get_software_techniques(overlay, stage, platform)
elif software_groups:
groups_software_dict = get_software_techniques(groups, stage, platform)
technique_count = get_technique_count(groups_dict, overlay_dict, groups_software_dict)
technique_layer = get_technique_layer(technique_count, groups_dict, overlay_dict, groups_software_dict,
overlay_file_type, overlay_type)
max_technique_count = max(technique_count.values(), key=lambda v: v['count'])['count']
# make a list group names for the involved groups.
if groups == ['all']:
groups_list = ['all']
else:
groups_list = get_group_list(groups_dict, groups_file_type)
overlay_list = get_group_list(overlay_dict, overlay_file_type)
desc = 'stage: ' + stage + ' | platform: ' + platform + ' | group(s): ' + ', '.join(groups_list) + \
' | overlay group(s): ' + ', '.join(overlay_list)
layer = get_layer_template_groups(stage[0].upper() + stage[1:] + ' ' + platform, max_technique_count, desc, stage, platform)
layer['techniques'] = technique_layer
json_string = simplejson.dumps(layer).replace('}, ', '},\n')
if overlay:
filename = "output/" + stage + '_' + platform.lower() + '_' + '_'.join(groups_list) + '-overlay_' + '_'.join(overlay_list) + '.json'
else:
filename = "output/" + stage + '_' + platform.lower() + '_' + '_'.join(groups_list) + '.json'
with open(filename, 'w') as f: # write layer file to disk
f.write(json_string)
print('Written layer: ' + filename)
def get_updates(update_type, sort='modified'):
"""
Print a list of updates for a techniques, groups or software. Sort by modified or creation date.
:param update_type: the type of update: techniques, groups or software
:param sort: sort the list by modified or creation date
:return:
"""
if update_type[:-1] == 'technique':
techniques = load_attack_data(DATATYPE_ALL_TECH)
sorted_techniques = sorted(techniques, key=lambda k: k[sort])
for t in sorted_techniques:
print(t['technique_id'] + ' ' + t['technique'])
print(' ' * 6 + 'created: ' + t['created'].split(' ')[0])
print(' ' * 6 + 'modified: ' + t['modified'][:10])
print(' ' * 6 + 'matrix: ' + t['matrix'][6:])
if t['tactic']:
print(' ' * 6 + 'tactic: ' + ' '.join(t['tactic']))
else:
print(' ' * 6 + 'tactic: None')
print('')
elif update_type[:-1] == 'group':
groups = load_attack_data(DATATYPE_ALL_GROUPS)
sorted_groups = sorted(groups, key=lambda k: k[sort])
for t in sorted_groups:
print(t['group_id'] + ' ' + t['group'])
print(' ' * 6 + 'created: ' + t['created'].split(' ')[0])
print(' ' * 6 + 'modified: ' + t['modified'][:10])
print('')
elif update_type == 'software':
software = load_attack_data(DATATYPE_ALL_SOFTWARE)
sorted_software = sorted(software, key=lambda k: k[sort])
for t in sorted_software:
print(t['software_id'] + ' ' + t['software'])
print(' ' * 6 + 'created: ' + t['created'].split(' ')[0])
print(' ' * 6 + 'modified: ' + t['modified'][:10])
print(' ' * 6 + 'matrix: ' + t['matrix'][6:])
print(' ' * 6 + 'type: ' + t['type'])
if t['software_platform']:
print(' ' * 6 + 'platform: ' + ', '.join(t['software_platform']))
else:
print(' ' * 6 + 'platform: None')
print('')
def get_statistics():
"""
Print out statistics related to data sources and how many techniques they cover.
:return:
"""
techniques = load_attack_data(DATATYPE_ALL_TECH)
# {data_source: {techniques: [T0001, ...}, count: ...}
data_sources_dict = {}
for tech in techniques:
tech_id = tech['technique_id']
data_sources = tech['data_sources']
if data_sources:
for ds in data_sources:
if ds not in data_sources_dict:
data_sources_dict[ds] = {'techniques': [tech_id], 'count': 1}
else:
data_sources_dict[ds]['techniques'].append(tech_id)
data_sources_dict[ds]['count'] += 1
# sort the dict on the value of 'count'
data_sources_dict_sorted = dict(sorted(data_sources_dict.items(), key=lambda kv: kv[1]['count'], reverse=True))
str_format = '{:<6s} {:s}'
print(str_format.format('Count', 'Data Source'))
print('-'*50)
for k, v in data_sources_dict_sorted.items():
print(str_format.format(str(v['count']), k))

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interactive_menu.py Normal file
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import sys
import glob
from data_source_mapping import *
from technique_mapping import *
from group_mapping import *
groups = 'all'
software_group = False
platform = 'Windows'
stage = 'attack'
groups_overlay = ''
overlay_type = ''
MENU_NAME_DATA_SOURCE_MAPPING = 'Data source mapping'
MENU_NAME_VISIBILITY_MAPPING = 'Visibility coverage mapping'
MENU_NAME_DETECTION_COVERAGE_MAPPING = 'Detection coverage mapping'
MENU_NAME_THREAT_ACTOR_GROUP_MAPPING = 'Threat actor group mapping'
def clear():
"""
Clears the terminal screen and prints the title and version of the application.
:return:
"""
if sys.platform.startswith('linux') or sys.platform == 'darwin':
os.system('clear')
elif sys.platform == 'win32':
os.system('cls')
name = '-= %s =-' % APP_NAME
desc = '-- %s --' % APP_DESC
version = 'version %s' % VERSION
print(' ' * int((len(desc)-len(name))/2) + name)
print(desc)
print(' ' * int((len(desc)-len(version))/2) + version)
print('')
def ask_input():
"""
Waits for input from the terminal.
:return:
"""
return input(' >> ')
def wait():
"""
Prints wait statement and wait for pressing ENTER key.
:return:
"""
print('')
print('Press a key to return to the last menu')
input('')
def interactive_menu():
"""
Main menu for interactive mode.
:return:
"""
clear()
print('Select a mode:')
print('1. %s' % MENU_NAME_DATA_SOURCE_MAPPING)
print('2. %s' % MENU_NAME_VISIBILITY_MAPPING)
print('3. %s' % MENU_NAME_DETECTION_COVERAGE_MAPPING)
print('4. %s' % MENU_NAME_THREAT_ACTOR_GROUP_MAPPING)
print('5. Updates')
print('6. Statistics')
print('9. Quit')
choice = ask_input()
if choice == '1':
menu_data_source(select_file(MENU_NAME_DATA_SOURCE_MAPPING, 'data sources', FILE_TYPE_DATA_SOURCE_ADMINISTRATION))
elif choice == '2':
menu_visibility(select_file(MENU_NAME_VISIBILITY_MAPPING, 'techniques (used to score the level of visibility)', FILE_TYPE_TECHNIQUE_ADMINISTRATION),
select_file(MENU_NAME_VISIBILITY_MAPPING, 'data sources (used to add metadata on the involved data sources to the heat map)', FILE_TYPE_DATA_SOURCE_ADMINISTRATION, False))
elif choice == '3':
menu_detection(select_file(MENU_NAME_DETECTION_COVERAGE_MAPPING, 'techniques', FILE_TYPE_TECHNIQUE_ADMINISTRATION))
elif choice == '4':
menu_groups()
elif choice == '5':
menu_updates()
elif choice == '6':
menu_statistics()
elif choice in ['9', 'q']:
quit()
else:
interactive_menu()
def select_file(title, what, expected_file_type, b_clear=True, path='sample-data/'):
"""
Prints and handles the file selection in the terminal. It shows just .yaml files.
:param title: title to print on top of this menu
:param what: print for what purpose the file is selected
:param expected_file_type: the expected file type of the YAML file
:param b_clear: clear the terminal before showing this memu
:param path: the path to look in
:return: filename of the selected file
"""
if b_clear:
clear()
print('Menu: %s' % title)
print('')
print('Select the YAML file with %s:' % what)
print('')
print('Path: %s' % path)
n = 1
files = []
for f in glob.glob(path + '*.yaml'):
files.append(f)
print('%d. %s' % (n, f))
n += 1
change_path_nr = 8 if n < 8 else n + (5 - n % 5) - 1
print('%d. Change path' % change_path_nr)
back_nr = 9 if n < 9 else n + (5 - n % 5)
print('%d. Back to main menu.' % back_nr)
choice = ask_input()
if choice == str(change_path_nr):
print("Supply full or relative path:")
choice = ask_input()
choice = choice if choice.endswith('/') else choice + '/'
if os.path.exists(choice):
return select_file(title, what, expected_file_type, b_clear, choice)
else:
print("[!] Path doesn't exist")
wait()
return select_file(title, what, expected_file_type, b_clear, path)
elif choice == str(back_nr):
interactive_menu()
elif choice == 'q':
quit()
else:
if choice.isdigit() and int(choice) < n:
filename = files[int(choice) - 1]
file_type = check_file_type(filename, file_type=expected_file_type)
if file_type:
return filename
else:
print("[!] Invalid choice")
wait()
return select_file(title, what, expected_file_type, b_clear, path)
def menu_updates():
"""
Prints and handles the menu for the Updates functionality.
:return:
"""
clear()
print('Menu: Updates')
print('')
print('Select for what you want to see updates:')
print('1. Techniques (sorted by modified date)')
print('1s. Techniques (sorted by creation date)')
print('2. Groups (sorted by modified date)')
print('2s. Groups (sorted by creation date)')
print('3. Software (sorted by modified date)')
print('3s. Software (sorted by creation date)')
print('9. Back to main menu.')
choice = ask_input()
if choice == '1':
get_updates('techniques')
wait()
if choice == '1s':
get_updates('techniques', 'created')
wait()
elif choice == '2':
get_updates('groups')
wait()
elif choice == '2s':
get_updates('groups', 'created')
wait()
elif choice == '3':
get_updates('software')
wait()
elif choice == '3s':
get_updates('software', 'created')
wait()
elif choice == '9':
interactive_menu()
elif choice == 'q':
quit()
menu_updates()
def menu_statistics():
"""
Handles the Statistics functionality.
:return:
"""
clear()
print('Menu: Statistics')
print('')
get_statistics()
wait()
interactive_menu()
def menu_data_source(filename):
"""
Prints and handles the Data source mapping functionality.
:param filename:
:return:
"""
clear()
print('Menu: %s' % MENU_NAME_DATA_SOURCE_MAPPING)
print('')
print('Selected data source YAML file: %s' % filename)
print('')
print('Select what you want to do:')
print('1. Generate a data source layer for the ATT&CK Navigator.')
print('2. Generate a graph with data sources added through time.')
print('3. Generate an Excel sheet with all data sources.')
print('4. Generate a technique administration YAML file with visibility scores, based on the number of available '
'data sources')
print('9. Back to main menu.')
choice = ask_input()
if choice == '1':
print('Writing data sources layer...')
generate_data_sources_layer(filename)
wait()
elif choice == '2':
print('Drawing the graph...')
plot_data_sources_graph(filename)
wait()
elif choice == '3':
print('Generating Excel file...')
export_data_source_list_to_excel(filename)
wait()
elif choice == '4':
print('Generating YAML file...')
generate_technique_administration_file(filename)
wait()
elif choice == '9':
interactive_menu()
elif choice == 'q':
quit()
menu_data_source(filename)
def menu_detection(filename_t):
"""
Prints and handles the Detection coverage mapping functionality.
:param filename_t:
:return:
"""
clear()
print('Menu: %s' % MENU_NAME_DETECTION_COVERAGE_MAPPING)
print('')
print('Selected techniques YAML file: %s' % filename_t)
print('')
print('Select what you want to do:')
print('1. Generate a layer for detection coverage for the ATT&CK Navigator.')
print('2. Generate a layer for detection coverage overlayed with visibility for the ATT&CK Navigator.')
print('3. Generate a graph with detections added through time.')
print('9. Back to main menu.')
choice = ask_input()
if choice == '1':
print('Writing detection coverage layer...')
generate_detection_layer(filename_t, None, False)
wait()
elif choice == '2':
filename_ds = select_file(MENU_NAME_DETECTION_COVERAGE_MAPPING, 'data sources (used to add metadata on the involved data sources to the heat map)', FILE_TYPE_DATA_SOURCE_ADMINISTRATION, False)
print('Writing detection coverage layer with visibility as overlay...')
generate_detection_layer(filename_t, filename_ds, True)
wait()
elif choice == '3':
print('Drawing the graph...')
plot_detection_graph(filename_t)
wait()
elif choice == '9':
interactive_menu()
elif choice == 'q':
quit()
menu_detection(filename_t)
def menu_visibility(filename_t, filename_ds):
"""
Prints and handles the Visibility coverage mappin functionality.
:param filename_t:
:param filename_ds:
:return:
"""
clear()
print('Menu: %s' % MENU_NAME_VISIBILITY_MAPPING)
print('')
print('Selected techniques YAML file: %s' % filename_t)
print('Selected data source YAML file: %s' % filename_ds)
print('')
print('Select what you want to do:')
print('1. Generate a layer for visibility for the ATT&CK Navigator.')
print('2. Generate a layers for visibility overlayed with detection coverage for the ATT&CK Navigator.')
print('9. Back to main menu.')
choice = ask_input()
if choice == '1':
print('Writing visibility coverage layer...')
generate_visibility_layer(filename_t, filename_ds, False)
wait()
elif choice == '2':
print('Writing visibility coverage layers overlayed with detections...')
generate_visibility_layer(filename_t, filename_ds, True)
wait()
elif choice == '9':
interactive_menu()
elif choice == 'q':
quit()
menu_visibility(filename_t, filename_ds)
def menu_groups():
"""
Prints and handles the Threat actor group mapping functionality.
:return:
"""
global groups, software_group, platform, stage, groups_overlay, overlay_type
clear()
print('Menu: %s' % MENU_NAME_THREAT_ACTOR_GROUP_MAPPING)
print('')
print('Options:')
print('1. Software group: %s' % str(software_group))
print('2. Platform: %s' % platform)
print('3. Stage: %s' % stage)
print('4. Groups: %s' % groups)
print('5. Overlay: ')
print(' - %s: %s' % ('File' if os.path.exists(groups_overlay) else 'Groups', groups_overlay))
print(' - Type: %s' % overlay_type)
print('')
print('6. Generate a heat map layer.')
print('9. Back to main menu.')
choice = ask_input()
if choice == '1':
print('Specify True or False for software group:')
software_group = True if ask_input().lower() == 'true' else False
elif choice == '2':
print('Specify platform (all, Linux, macOS, Windows):')
p = ask_input().lower()
platform = 'Windows' if p == 'windows' else 'Linux' if p == 'linux' else 'macOS' if p == 'macos' else 'all'
elif choice == '3':
print('Specify stage (pre-attack, attack):')
s = ask_input().lower()
stage = 'pre-attack' if s == 'pre-attack' else 'attack'
elif choice == '4':
print('Specify the groups to include separated using commas. Group can be their ID, name or alias '
'(default is all groups). Other option is to provide a YAML file with a custom group(s)')
groups = ask_input()
elif choice == '5':
print('')
print('1. Overlay with groups.')
print('2. Overlay with detections.')
print('3. Overlay with visibility.')
print('4. No overlay.')
choice = ask_input()
if choice == '1':
print('Specify the group(s) to overlay (in a different color) on the one specified in the Groups option. '
'A group can be their ID, name or alias separated using commas. Other option is to provide a YAML '
'file with a custom group(s).')
overlay_type = 'group'
groups_overlay = ask_input()
elif choice == '2':
overlay_type = 'detection'
groups_overlay = select_file(MENU_NAME_THREAT_ACTOR_GROUP_MAPPING, 'techniques', FILE_TYPE_TECHNIQUE_ADMINISTRATION, False)
elif choice == '3':
overlay_type = 'visibility'
groups_overlay = select_file(MENU_NAME_THREAT_ACTOR_GROUP_MAPPING, 'techniques', FILE_TYPE_TECHNIQUE_ADMINISTRATION, False)
elif choice == '4':
overlay_type = ''
groups_overlay = ''
elif choice == '6':
generate_group_heat_map(groups, groups_overlay, overlay_type, stage, platform, software_group)
wait()
elif choice == '9':
interactive_menu()
elif choice == 'q':
quit()
menu_groups()

6
requirements.txt Normal file
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attackcti==0.2.1
simplejson==3.16.0
PyYAML==5.1
plotly==3.7.1
pandas==0.24.2
xlsxwriter==1.1.5

626
sample-data/data-sources-endpoints.yaml Normal file
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%YAML 1.2
---
version: 1.0
file_type: data-source-administration
name: endpoints-example
platform: windows
data_sources:
# A data source is treated as not available when all dimensions of the data quality have a score of 0.
# If desired you are free to add any key-value pairs.
- data_source_name: Process monitoring
date_registered: 2019-03-01
date_connected: 2017-01-01
products: [Windows event log]
available_for_data_analytics: True
comment: ''
data_quality:
device_completeness: 5
data_field_completeness: 5
timeliness: 5
consistency: 5
retention: 5
- data_source_name: File monitoring
date_registered:
date_connected:
products: [None]
available_for_data_analytics: False
comment: ''
data_quality:
device_completeness: 0
data_field_completeness: 0
timeliness: 0
consistency: 0
retention: 0
- data_source_name: Process command-line parameters
date_registered: 2019-03-01
date_connected: 2017-01-01
products: [Windows event log]
available_for_data_analytics: True
comment: ''
data_quality:
device_completeness: 0
data_field_completeness: 0
timeliness: 0
consistency: 0
retention: 0
- data_source_name: API monitoring
date_registered:
date_connected:
products: [None]
available_for_data_analytics: False
comment: ''
data_quality:
device_completeness: 0
data_field_completeness: 0
timeliness: 0
consistency: 0
retention: 0
- data_source_name: Process use of network
date_registered:
date_connected:
products: [None]
available_for_data_analytics: False
comment: ''
data_quality:
device_completeness: 0
data_field_completeness: 0
timeliness: 0
consistency: 0
retention: 0
- data_source_name: Windows Registry
date_registered: 2019-03-01
date_connected: 2017-02-01
products: [Windows event log]
available_for_data_analytics: True
comment: ''
data_quality:
device_completeness: 5
data_field_completeness: 5
timeliness: 5
consistency: 5
retention: 5
- data_source_name: Packet capture
date_registered:
date_connected:
products: [None]
available_for_data_analytics: False
comment: ''
data_quality:
device_completeness: 0
data_field_completeness: 0
timeliness: 0
consistency: 0
retention: 0
- data_source_name: Authentication logs
date_registered:
date_connected:
products: [None]
available_for_data_analytics: False
comment: ''
data_quality:
device_completeness: 0
data_field_completeness: 0
timeliness: 0
consistency: 0
retention: 0
- data_source_name: Netflow/Enclave netflow
date_registered:
date_connected:
products: [None]
available_for_data_analytics: False
comment: ''
data_quality:
device_completeness: 0
data_field_completeness: 0
timeliness: 0
consistency: 0
retention: 0
- data_source_name: Windows event logs
date_registered:
date_connected:
products: [None]
available_for_data_analytics: False
comment: ''
data_quality:
device_completeness: 0
data_field_completeness: 0
timeliness: 0
consistency: 0
retention: 0
- data_source_name: Binary file metadata
date_registered:
date_connected:
products: [None]
available_for_data_analytics: False
comment: ''
data_quality:
device_completeness: 0
data_field_completeness: 0
timeliness: 0
consistency: 0
retention: 0
- data_source_name: Network protocol analysis
date_registered:
date_connected:
products: [None]
available_for_data_analytics: False
comment: ''
data_quality:
device_completeness: 0
data_field_completeness: 0
timeliness: 0
consistency: 0
retention: 0
- data_source_name: DLL monitoring
date_registered:
date_connected:
products: [None]
available_for_data_analytics: False
comment: ''
data_quality:
device_completeness: 0
data_field_completeness: 0
timeliness: 0
consistency: 0
retention: 0
- data_source_name: Loaded DLLs
date_registered:
date_connected:
products: [None]
available_for_data_analytics: False
comment: ''
data_quality:
device_completeness: 0
data_field_completeness: 0
timeliness: 0
consistency: 0
retention: 0
- data_source_name: System calls
date_registered:
date_connected:
products: [None]
available_for_data_analytics: False
comment: ''
data_quality:
device_completeness: 0
data_field_completeness: 0
timeliness: 0
consistency: 0
retention: 0
- data_source_name: Malware reverse engineering
date_registered:
date_connected:
products: [None]
available_for_data_analytics: False
comment: ''
data_quality:
device_completeness: 0
data_field_completeness: 0
timeliness: 0
consistency: 0
retention: 0
- data_source_name: SSL/TLS inspection
date_registered: 2019-01-10
date_connected: 2000-01-01
products: [Proxy Product]
available_for_data_analytics: True
comment: ''
data_quality:
device_completeness: 5
data_field_completeness: 5
timeliness: 5
consistency: 5
retention: 5
- data_source_name: Anti-virus
date_registered: 2019-01-10
date_connected: 2000-01-01
products: [AV Product]
available_for_data_analytics: True
comment: ''
data_quality:
device_completeness: 4
data_field_completeness: 2
timeliness: 3
consistency: 5
retention: 5
- data_source_name: Network intrusion detection system
date_registered: 2019-01-10
date_connected: 2016-01-01
products: [NIDS]
available_for_data_analytics: True
comment: ''
data_quality:
device_completeness: 4
data_field_completeness: 3
timeliness: 3
consistency: 4
retention: 4
- data_source_name: Data loss prevention
date_registered:
date_connected:
products: [None]
available_for_data_analytics: False
comment: ''
data_quality:
device_completeness: 0
data_field_completeness: 0
timeliness: 0
consistency: 0
retention: 0
- data_source_name: Application logs
date_registered:
date_connected:
products: [None]
available_for_data_analytics: False
comment: ''
data_quality:
device_completeness: 0
data_field_completeness: 0
timeliness: 0
consistency: 0
retention: 0
- data_source_name: Email gateway
date_registered: 2019-01-10
date_connected: 2000-01-01
products: [Email Gateway Product]
available_for_data_analytics: True
comment: ''
data_quality:
device_completeness: 4
data_field_completeness: 3
timeliness: 3
consistency: 5
retention: 3
- data_source_name: Network device logs
date_registered:
date_connected:
products: [None]
available_for_data_analytics: False
comment: ''
data_quality:
device_completeness: 0
data_field_completeness: 0
timeliness: 0
consistency: 0
retention: 0
- data_source_name: Web proxy
date_registered: 2019-01-10
date_connected: 2000-01-01
products: [Proxy Product]
available_for_data_analytics: True
comment: ''
data_quality:
device_completeness: 5
data_field_completeness: 5
timeliness: 5
consistency: 5
retention: 5
- data_source_name: Windows Error Reporting
date_registered:
date_connected:
products: [None]
available_for_data_analytics: False
comment: ''
data_quality:
device_completeness: 0
data_field_completeness: 0
timeliness: 0
consistency: 0
retention: 0
- data_source_name: Kernel drivers
date_registered:
date_connected:
products: [None]
available_for_data_analytics: False
comment: ''
data_quality:
device_completeness: 0
data_field_completeness: 0
timeliness: 0
consistency: 0
retention: 0
- data_source_name: User interface
date_registered:
date_connected:
products: [None]
available_for_data_analytics: False
comment: ''
data_quality:
device_completeness: 0
data_field_completeness: 0
timeliness: 0
consistency: 0
retention: 0
- data_source_name: Host network interface
date_registered:
date_connected:
products: [None]
available_for_data_analytics: False
comment: ''
data_quality:
device_completeness: 0
data_field_completeness: 0
timeliness: 0
consistency: 0
retention: 0
- data_source_name: Third-party application logs
date_registered:
date_connected:
products: [None]
available_for_data_analytics: False
comment: ''
data_quality:
device_completeness: 0
data_field_completeness: 0
timeliness: 0
consistency: 0
retention: 0
- data_source_name: Services
date_registered:
date_connected:
products: [None]
available_for_data_analytics: False
comment: ''
data_quality:
device_completeness: 0
data_field_completeness: 0
timeliness: 0
consistency: 0
retention: 0
- data_source_name: Web logs
date_registered:
date_connected:
products: [None]
available_for_data_analytics: False
comment: ''
data_quality:
device_completeness: 0
data_field_completeness: 0
timeliness: 0
consistency: 0
retention: 0
- data_source_name: Detonation chamber
date_registered:
date_connected:
products: [None]
available_for_data_analytics: False
comment: ''
data_quality:
device_completeness: 0
data_field_completeness: 0
timeliness: 0
consistency: 0
retention: 0
- data_source_name: Mail server
date_registered:
date_connected:
products: [None]
available_for_data_analytics: False
comment: ''
data_quality:
device_completeness: 0
data_field_completeness: 0
timeliness: 0
consistency: 0
retention: 0
- data_source_name: Environment variable
date_registered:
date_connected:
products: [None]
available_for_data_analytics: False
comment: ''
data_quality:
device_completeness: 0
data_field_completeness: 0
timeliness: 0
consistency: 0
retention: 0
- data_source_name: MBR
date_registered:
date_connected:
products: [None]
available_for_data_analytics: False
comment: ''
data_quality:
device_completeness: 0
data_field_completeness: 0
timeliness: 0
consistency: 0
retention: 0
- data_source_name: BIOS
date_registered:
date_connected:
products: [None]
available_for_data_analytics: False
comment: ''
data_quality:
device_completeness: 0
data_field_completeness: 0
timeliness: 0
consistency: 0
retention: 0
- data_source_name: Web application firewall logs
date_registered:
date_connected:
products: [None]
available_for_data_analytics: False
comment: ''
data_quality:
device_completeness: 0
data_field_completeness: 0
timeliness: 0
consistency: 0
retention: 0
- data_source_name: Asset management
date_registered:
date_connected:
products: [None]
available_for_data_analytics: False
comment: ''
data_quality:
device_completeness: 0
data_field_completeness: 0
timeliness: 0
consistency: 0
retention: 0
- data_source_name: DHCP
date_registered:
date_connected:
products: [None]
available_for_data_analytics: True
comment: 'At the time of writing: unknown data source within ATT&CK'
data_quality:
device_completeness: 5
data_field_completeness: 5
timeliness: 5
consistency: 5
retention: 5
- data_source_name: DNS records
date_registered: 2019-03-01
date_connected: 2017-04-01
products: [Windows DNS server]
available_for_data_analytics: True
comment: ''
data_quality:
device_completeness: 5
data_field_completeness: 5
timeliness: 5
consistency: 5
retention: 5
- data_source_name: Browser extensions
date_registered:
date_connected:
products: [None]
available_for_data_analytics: False
comment: ''
data_quality:
device_completeness: 0
data_field_completeness: 0
timeliness: 0
consistency: 0
retention: 0
- data_source_name: Access tokens
date_registered:
date_connected:
products: [None]
available_for_data_analytics: False
comment: ''
data_quality:
device_completeness: 0
data_field_completeness: 0
timeliness: 0
consistency: 0
retention: 0
- data_source_name: Digital certificate logs
date_registered:
date_connected:
products: [None]
available_for_data_analytics: False
comment: ''
data_quality:
device_completeness: 0
data_field_completeness: 0
timeliness: 0
consistency: 0
retention: 0
- data_source_name: Disk forensics
date_registered: 2019-01-10
date_connected: 2019-01-01
products: [Manual, Commercial tool]
available_for_data_analytics: True
comment: ''
data_quality:
device_completeness: 5
data_field_completeness: 5
timeliness: 1
consistency: 5
retention: 0
- data_source_name: Component firmware
date_registered:
date_connected:
products: [None]
available_for_data_analytics: False
comment: ''
data_quality:
device_completeness: 0
data_field_completeness: 0
timeliness: 0
consistency: 0
retention: 0
- data_source_name: WMI Objects
date_registered:
date_connected:
products: [None]
available_for_data_analytics: False
comment: ''
data_quality:
device_completeness: 0
data_field_completeness: 0
timeliness: 0
consistency: 0
retention: 0
- data_source_name: VBR
date_registered:
date_connected:
products: [None]
available_for_data_analytics: False
comment: ''
data_quality:
device_completeness: 0
data_field_completeness: 0
timeliness: 0
consistency: 0
retention: 0
- data_source_name: Named Pipes
date_registered:
date_connected:
products: [None]
available_for_data_analytics: False
comment: ''
data_quality:
device_completeness: 0
data_field_completeness: 0
timeliness: 0
consistency: 0
retention: 0
- data_source_name: Sensor health and status
date_registered:
date_connected:
products: [None]
available_for_data_analytics: False
comment: ''
data_quality:
device_completeness: 0
data_field_completeness: 0
timeliness: 0
consistency: 0
retention: 0
- data_source_name: EFI
date_registered:
date_connected:
products: [None]
available_for_data_analytics: False
comment: ''
data_quality:
device_completeness: 0
data_field_completeness: 0
timeliness: 0
consistency: 0
retention: 0
- data_source_name: PowerShell logs
date_registered: 2019-03-01
date_connected: 2018-05-01
products: [Windows event log]
available_for_data_analytics: True
comment: 'Script block logging is enabled'
data_quality:
device_completeness: 5
data_field_completeness: 5
timeliness: 5
consistency: 5
retention: 4
exceptions:
# Adding a technique ID below will result in removing that technique in the heat map (meaning not enough data source are available for proper detection).
# Please note that the below is just an example, many more can exists.
- technique_id: T1130
name: Install Root Certificate

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%YAML 1.2
---
version: 1.0
file_type: group-administration
groups:
-
group_name: Red team
campaign: Scenario 1
technique_id: [T1086, T1053, T1193, T1204, T1003, T1055, T1027, T1085, T1099, T1082, T1016, T1033, T1087, T1075, T1057, T1039, T1041, T1071, T1043, T1001, T1114, T1002]
software_id: [S0002]
enabled: True
-
group_name: APT3 (MITRE ATT&CK evaluation)
campaign: First Scenario
technique_id: [T1204, T1064, T1085, T1060, T1043, T1071, T1132, T1016, T1059, T1033, T1057, T1106, T1007, T1082, T1069, T1087, T1012, T1088, T1134, T1055, T1018, T1049, T1003, T1026, T1076, T1136, T1061, T1105, T1053, T1083, T1056, T1010, T1113, T1039, T1041, T1078]
software_id: [S0154]
enabled: False

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import simplejson
from generic import *
# Imports for pandas and plotly are because of performance reasons in the function that uses these libraries.
def generate_detection_layer(filename_techniques, filename_data_sources, overlay):
"""
Generates layer for detection coverage and optionally an overlayed version with visibility coverage.
:param filename_techniques: the filename of the yaml file containing the techniques administration
:param filename_data_sources: the filename of the yaml file containing the data sources administration
:param overlay: boolean value to specify if an overlay between detection and visibility should be generated
:return:
"""
my_techniques, name, platform = _load_detections(filename_techniques)
if not overlay:
mapped_techniques_detection = _map_and_colorize_techniques_for_detections(my_techniques)
layer_detection = get_layer_template_detections('Detections ' + name, 'description', 'attack', platform)
_write_layer(layer_detection, mapped_techniques_detection, 'detection', name)
else:
my_data_sources = _load_data_sources(filename_data_sources)
mapped_techniques_both = _map_and_colorize_techniques_for_overlayed(my_techniques, my_data_sources)
layer_both = get_layer_template_layered('Visibility and Detection ' + name, 'description', 'attack', platform)
_write_layer(layer_both, mapped_techniques_both, 'visibility_and_detection', name)
def generate_visibility_layer(filename_techniques, filename_data_sources, overlay):
"""
Generates layer for visibility coverage and optionally an overlayed version with detection coverage.
:param filename_techniques: the filename of the yaml file containing the techniques administration
:param filename_data_sources: the filename of the yaml file containing the data sources administration
:param overlay: boolean value to specify if an overlay between detection and visibility should be generated
:return:
"""
my_techniques, name, platform = _load_detections(filename_techniques)
my_data_sources = _load_data_sources(filename_data_sources)
if not overlay:
mapped_techniques_visibility = _map_and_colorize_techniques_for_visibility(my_techniques, my_data_sources)
layer_visibility = get_layer_template_visibility('Visibility ' + name, 'description', 'attack', platform)
_write_layer(layer_visibility, mapped_techniques_visibility, 'visibility', name)
else:
mapped_techniques_both = _map_and_colorize_techniques_for_overlayed(my_techniques, my_data_sources)
layer_both = get_layer_template_layered('Visibility and Detection ' + name, 'description', 'attack', platform)
_write_layer(layer_both, mapped_techniques_both, 'visibility_and_detection', name)
def plot_detection_graph(filename):
"""
Generates a line graph which shows the improvements on detections through the time.
:param filename: the filename of the yaml file containing the techniques administration
:return:
"""
my_techniques, name, platform = _load_detections(filename)
graph_values = []
for t in my_techniques.values():
if 'detection' in t.keys() and t['detection']['date_implemented']:
yyyymm = t['detection']['date_implemented'].strftime('%Y-%m')
graph_values.append({'date': yyyymm, 'count': 1})
import pandas as pd
df = pd.DataFrame(graph_values).groupby('date', as_index=False)[['count']].sum()
df['cumcount'] = df.ix[::1, 'count'].cumsum()[::1]
output_filename = 'output/graph_detection.html'
import plotly
import plotly.graph_objs as go
plotly.offline.plot(
{'data': [go.Scatter(x=df['date'], y=df['cumcount'])],
'layout': go.Layout(title="# of detections for " + name)},
filename=output_filename, auto_open=False
)
print("File written: " + output_filename)
def _load_detections(filename):
"""
Loads the techniques (including detection and visibility properties) from the given yaml file.
:param filename: the filename of the yaml file containing the techniques administration
:return: dictionary with techniques (incl. properties), name and platform
"""
my_techniques = {}
with open(filename, 'r') as yaml_file:
yaml_content = yaml.load(yaml_file, Loader=yaml.FullLoader)
for d in yaml_content['techniques']:
my_techniques[d['technique_id']] = d
name = yaml_content['name']
platform = yaml_content['platform']
return my_techniques, name, platform
def _load_data_sources(filename):
"""
Loads the data sources (including all properties) from the given yaml file.
:param filename: the filename of the yaml file containing the data sources administration
:return: dictionary with data sources (including properties)
"""
my_data_sources = {}
with open(filename, 'r') as yaml_file:
yaml_content = yaml.load(yaml_file, Loader=yaml.FullLoader)
for d in yaml_content['data_sources']:
dq = d['data_quality']
if dq['device_completeness'] > 0 and dq['data_field_completeness'] > 0 and dq['timeliness'] > 0 and dq['consistency'] > 0:
my_data_sources[d['data_source_name']] = d
return my_data_sources
def _write_layer(layer, mapped_techniques, filename_prefix, name):
"""
Writes the json layer file to disk.
:param layer: the prepped layer dictionary
:param mapped_techniques: the techniques section that will be included in the layer
:param filename_prefix: the prefix for the output filename
:param name: the name that will be used in the filename together with the prefix
:return:
"""
layer['techniques'] = mapped_techniques
json_string = simplejson.dumps(layer).replace('}, ', '},\n')
output_filename = 'output/%s_%s.json' % (filename_prefix, normalize_name_to_filename(name))
with open(output_filename, 'w') as f:
f.write(json_string)
print("File written: " + output_filename)
def _map_and_colorize_techniques_for_detections(my_techniques):
"""
Determine the color of the techniques based on the detection score in the given yaml file.
:param my_techniques: the configured techniques
:return: a dictionary with techniques that can be used in the layer's output file
"""
techniques = load_attack_data(DATATYPE_ALL_TECH)
# Color the techniques based on how the coverage defined in the detections definition and generate a list with
# techniques to be used in the layer output file.
mapped_techniques = []
try:
for d, c in my_techniques.items():
s = -1 if 'detection' not in c.keys() else c['detection']['score']
color = COLOR_D_0 if s == 0 else COLOR_D_1 if s == 1 else COLOR_D_2 if s == 2 else COLOR_D_3 \
if s == 3 else COLOR_D_4 if s == 4 else COLOR_D_5 if s == 5 else ''
technique = get_technique(techniques, d)
for tactic in technique['tactic']:
location = ', '.join(c['detection']['location']) if 'detection' in c.keys() else '-'
location = location if location != '' else '-'
x = {}
x['techniqueID'] = d
x['color'] = color
x['comment'] = ''
x['enabled'] = True
x['tactic'] = tactic.lower().replace(' ', '-')
x['metadata'] = [{'name': '-Detection score', 'value': str(s)},
{'name': '-Detection location', 'value': location}]
mapped_techniques.append(x)
except Exception:
print('[!] Possible error in YAML file at: ' + d)
quit()
return mapped_techniques
def _map_and_colorize_techniques_for_visibility(my_techniques, my_data_sources):
"""
Determine the color of the techniques based on the visibility score in the given yaml file.
:param my_techniques: the configured techniques
:param my_data_sources: the configured data sources
:return: a dictionary with techniques that can be used in the layer's output file
"""
techniques = load_attack_data(DATATYPE_ALL_TECH)
technique_ds_mapping = map_techniques_to_data_sources(techniques, my_data_sources)
# Color the techniques based on how the coverage defined in the detections definition and generate a list with
# techniques to be used in the layer output file.
mapped_techniques = []
for d, c in my_techniques.items():
s = 0 if 'visibility' not in c.keys() else c['visibility']['score']
if 'visibility' in c.keys():
comment = str(c['visibility']['comment']) if str(c['visibility']['comment']) != '' else '-'
else:
comment = '-'
my_ds = ', '.join(technique_ds_mapping[d]['my_data_sources']) if d in technique_ds_mapping.keys() and technique_ds_mapping[d]['my_data_sources'] else '-'
color = COLOR_V_1 if s == 1 else COLOR_V_2 if s == 2 else COLOR_V_3 if s == 3 else COLOR_V_4 if s == 4 else ''
technique = get_technique(techniques, d)
for tactic in technique['tactic']:
x = {}
x['techniqueID'] = d
x['color'] = color
x['comment'] = ''
x['enabled'] = True
x['tactic'] = tactic.lower().replace(' ', '-')
x['metadata'] = [{'name': '-Visibility score', 'value': str(s)},
{'name': '-Comment', 'value': comment},
{'name': '-Available data sources', 'value': my_ds},
{'name': '-ATT&CK data sources', 'value': ', '.join(technique['data_sources'])}]
mapped_techniques.append(x)
for t in techniques:
if t['technique_id'] not in my_techniques.keys():
if t['tactic']:
for tactic in t['tactic']:
x = {}
x['techniqueID'] = t['technique_id']
x['comment'] = ''
x['enabled'] = True
x['tactic'] = tactic.lower().replace(' ', '-')
ds = ', '.join(t['data_sources']) if t['data_sources'] else '-'
x['metadata'] = [{'name': '-ATT&CK data sources', 'value': ds}]
mapped_techniques.append(x)
return mapped_techniques
def _map_and_colorize_techniques_for_overlayed(my_techniques, my_data_sources):
"""
Determine the color of the techniques based on both detection and visibility.
:param my_techniques: the configured techniques
:param my_data_sources: the configured data sources
:return: a dictionary with techniques that can be used in the layer's output file
"""
techniques = load_attack_data(DATATYPE_ALL_TECH)
technique_ds_mapping = map_techniques_to_data_sources(techniques, my_data_sources)
# Color the techniques based on how the coverage defined in the detections definition and generate a list with
# techniques to be used in the layer output file.
mapped_techniques = []
for d, c in my_techniques.items():
detection_score = 0 if 'detection' not in c.keys() else c['detection']['score']
visibility_score = 0 if 'visibility' not in c.keys() else c['visibility']['score']
detection = True if detection_score > 0 else False
visibility = True if visibility_score > 0 else False
if detection and visibility:
color = COLOR_OVERLAY_BOTH
elif detection and not visibility:
color = COLOR_OVERLAY_DETECTION
elif not detection and visibility:
color = COLOR_OVERLAY_VISIBILITY
location = ', '.join(c['detection']['location']) if 'detection' in c.keys() else '-'
location = location if location != '' else '-'
if 'visibility' in c.keys():
comment = str(c['visibility']['comment']) if str(c['visibility']['comment']) != '' else '-'
else:
comment = '-'
my_ds = ', '.join(technique_ds_mapping[d]['my_data_sources']) if d in technique_ds_mapping.keys() and technique_ds_mapping[d]['my_data_sources'] else '-'
technique = get_technique(techniques, d)
for tactic in technique['tactic']:
x = {}
x['techniqueID'] = d
x['color'] = color
x['comment'] = ''
x['enabled'] = True
x['tactic'] = tactic.lower().replace(' ', '-')
x['metadata'] = [{'name': '-Visibility score', 'value': str(visibility_score)},
{'name': '-Comment', 'value': comment},
{'name': '-Available data sources', 'value': my_ds},
{'name': '-ATT&CK data sources', 'value': ', '.join(technique['data_sources'])},
{'name': '-Detection score', 'value': str(detection_score)},
{'name': '-Detection location', 'value': location}]
mapped_techniques.append(x)
return mapped_techniques