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