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navgen_analytics.py
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navgen_analytics.py
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# !/usr/bin/env python3
"""This script extracts Mitre ATT&CK TTPs from CB Endpoint Standard alert data
and generates sample Mitre ATT&CK Navigation layers
TODO:
"""
import argparse
import json
import sys
from datetime import datetime
import pandas as pd
import plotly.express as px
import plotly.io as pio
# Pandas options
pd.set_option('display.max_rows', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.width', None)
pd.set_option('display.max_colwidth', None)
pio.templates.default = "seaborn"
def write_to_disk(filename, json_data):
filetimestamp = datetime.now().strftime("%Y%m%d-%H%M%S")
with open(f"{filetimestamp}_{filename}", 'w', encoding='utf-8') as outfile:
json.dump(json_data, outfile, indent=4, ensure_ascii=False)
return True
def get_mitre_ttps():
from attackcti import attack_client
lift = attack_client()
all_techniques = lift.get_techniques()
all_techniques = lift.remove_revoked(all_techniques)
all_techniques = lift.get_techniques(stix_format=False)
all_techniques = lift.remove_revoked(all_techniques)
return all_techniques
def draw_charts(project, mitre_merge_alert_ttp):
# Bar chart by severity
df_bar = (mitre_merge_alert_ttp['severity']
.value_counts()
.rename_axis('severity')
.reset_index(name='count')
.sort_values(by=['severity']))
fig = px.bar(df_bar, x='severity', y='count',
title="CB Analytics Alerts by Severity",
labels={"severity": "Severity", "count": "Count"})
fig.write_image(f"{project}_bar_cb_analytics_by_severity.png", engine="kaleido")
# Bar chart by tactic
df_bar = (mitre_merge_alert_ttp['tactic']
.value_counts()
.rename_axis('tactic')
.reset_index(name='count')
.sort_values(by=['count'], ascending=True))
fig = px.bar(df_bar, y='count', x='tactic',
height=600,
title="CB Analytics Alerts by MITRE ATT&CK Tactic",
labels={"tactic": "MITRE ATT&CK Tactic","count": "Count"})
fig.write_image(f"{project}_bar_cb_analytics_by_tactic.png", engine="kaleido")
# Bar chart by technique
df_bar = (mitre_merge_alert_ttp['technique']
.value_counts()
.rename_axis('technique')
.reset_index(name='count')
.sort_values(by=['count'], ascending=True))
fig = px.bar(df_bar, x='count', y='technique', orientation='h',
height=600,
title="CB Analytics Alerts by MITRE ATT&CK Technique",
labels={"technique": "MITRE ATT&CK Technique", "count": "Count"})
fig.write_image(f"{project}_bar_cb_analytics_by_technique.png", engine="kaleido")
# Radar chart by tactic
df_tactic = (mitre_merge_alert_ttp['tactic']
.value_counts()
.rename_axis('tactic')
.reset_index(name='count')
.sort_values(by=['count'], ascending=True)).set_index('tactic')
tactic_count = df_tactic['count'].tolist()
tactic = df_tactic.index.values.tolist()
fig = px.line_polar(df_tactic,
r=tactic_count,
theta=tactic,
line_close=True,
title="CB Analytics Alerts by MITRE ATT&CK Tactic")
fig.update_traces(fill='toself')
fig.update_layout(
polar=dict(
radialaxis=dict(
visible=True
),
),
showlegend=False
)
fig.write_image(f"{project}_radar_cb_analytics_by_tactic.png", engine="kaleido")
def write_layer(layer_name, techniques, out_file, max_value=0):
VERSION = "4.2"
NAME = layer_name
DESCRIPTION = "CB Analytics/Endpoint Standard"
DOMAIN = "enterprise-attack"
platform_layer = {
"name": NAME,
"description": DESCRIPTION,
"domain": DOMAIN,
"version": VERSION,
"filters": {"platforms": ["windows", "linux", "macOS"]},
"sorting": 3,
"techniques": techniques,
"gradient": {
"colors": ["#ffffff", "#78BE20"],
"minValue": 0,
"maxValue": 1,
},
"legendItems": [],
"metadata": [],
"showTacticRowBackground": True,
"tacticRowBackground": "#1d428a"
}
if max_value:
platform_layer["maxValue"] = max_value
platform_layer["legendItems"] = [{"color": "#ffffff", "label": "No alerts or events"},
{"color": "#0091da", "label": "TTP identified"}]
return write_to_disk(out_file, platform_layer)
def main():
parser = argparse.ArgumentParser(
prog="navgen_analytics.py",
description="Takes CB_ANALYTICS json from get_base_alerts.py and generates Mitre ATT&CK navigator layers"
)
parser.add_argument("-f", "--alert_file", required=True, help="The alert data json file written by get_base_alerts.py")
parser.add_argument("-p", "--project", required=False, help="Project Name")
parser.add_argument("-c", "--csv", action='store_true', help="Export the enriched alert data to a csv file")
parser.add_argument("-l", "--local_ttps", action='store_true', help="Use local version of ttps in all_techniques.json")
args = parser.parse_args()
# Load json data into pandas data frame
json_data = pd.read_json(args.alert_file)
# Create a list of the "results" key, aka the alerts
bn = json_data.results.values.tolist()
# Push just the alerts into a dataframe
df_alert = pd.DataFrame(json_data.results.values.tolist())
# Filter the alerts to only the CB_Analytics -
# should be unneccessary as we're only parsing CBA alerts
df_alert = df_alert.loc[df_alert['type'] == 'CB_ANALYTICS']
df_column_headers = df_alert.columns.tolist()
# Threat indicators are nested, flatten it
df_threat_indicators = pd.json_normalize(
bn,
'threat_indicators',
df_column_headers,
record_prefix='threat_indicators_',
errors='ignore'
)
df_ttps = df_threat_indicators.explode('threat_indicators_ttps').drop(columns=['threat_indicators']).reset_index()
# Extract the mitre ttp and assign to mitre_technique key
df_ttps['mitre_technique'] = df_ttps['threat_indicators_ttps'].str.extract(r'(?<=MITRE_)(.*?)(?=\_)')
df_ttps.head()
# Get all techniques
if args.local_ttps:
with open("all_techniques.json", "r") as f:
all_techniques = json.load(f)
else:
all_techniques = get_mitre_ttps()
# Push mitre ttps into flattened df
techniques_df = pd.json_normalize(all_techniques)
# Drop sub-techniques
techniques_df = techniques_df[techniques_df['x_mitre_is_subtechnique']==False]
# limit dataframe to 4 keys
techniques_df = techniques_df[['matrix','tactic','technique','technique_id']]
# technique_id has a one to many relationship with tactic and the tactic column stores values as a list
# Flatten the tactic values so that we are left with a table of all techniques and tactics
techniques_df = techniques_df.explode("tactic")
# Pull the mitre-sourced data into the CBA alert data
mitre_merge_alert_ttp = pd.merge(
df_ttps,
techniques_df,
left_on=["mitre_technique"],
right_on=["technique_id"],
)
# Export to CSV if wanted
if args.csv == True:
mitre_merge_alert_ttp.to_csv(f'{args.project}_alerts.csv')
mitre_merge_alert_ttp.sort_values(by='severity', ascending=False).reset_index(drop=True).head()
mitre_merge_alert_ttp.loc[mitre_merge_alert_ttp['severity'] >= 8].sort_values(by='severity', ascending=False).reset_index(drop=True).head()
mitre_merge_alert_ttp.loc[mitre_merge_alert_ttp['technique'] == "Account Manipulation"].sort_values(by='severity', ascending=False).reset_index(drop=True).head()
# Create the png files
draw_charts(args.project, mitre_merge_alert_ttp)
# Create the MITRE ATT&CK Navigator Layers
columns = [
"id",
"legacy_alert_id",
"process_name",
"threat_indicators_sha256",
"severity",
"reason",
"tactic",
"technique",
"technique_id",
"device_name",
"device_username",
"sensor_action"
]
technique_enabled = True #for future use to enable or disable a technique based on a config file
show_tub_techniques = False #for future use to enable or disable a technique based on a config file
df = mitre_merge_alert_ttp[columns].rename_axis(None).reset_index(drop=True)
data = df[["tactic","technique_id"]].reset_index(drop=True).drop_duplicates()
data = data.to_dict('records')
# Basic layer
tl = []
for d in data:
techniques = {
"techniqueID": d.get('technique_id'),
"tactic": d.get('tactic'),
"score": 1,
"color": "",
"comment": "",
"enabled": technique_enabled,
"metadata": "",
"showSubtechniques": show_tub_techniques
}
tl.append(techniques)
NAME = "Carbon Black ATT&CK Analytics: Basic Example"
write_layer(NAME, tl, f"{args.project}_attack_cb_basic.json")
# Metadata devices Layer
data = df[["tactic","technique_id","device_name"]].reset_index(drop=True).drop_duplicates()
grouped = data.groupby(['tactic','technique_id'], as_index=False).agg({'device_name': lambda x: x.tolist()})
grouped = grouped.to_dict(orient="records")
tl = []
for d in grouped:
#d['techniqueID'] = d.pop('technique_id')
techniques = {
"techniqueID": d.get('technique_id'),
"tactic": d.get('tactic'),
"score": 1,
"color": "",
"comment": "",
"enabled": technique_enabled,
"metadata": [
{
"name": "Device(s)",
"value": ', '.join(d.get('device_name'))
}
],
"showSubtechniques": show_tub_techniques
}
tl.append(techniques)
NAME = "Carbon Black ATT&CK Analytics: Metadata Example"
write_layer(NAME, tl, f"{args.project}_attack_cb_metadata_devices.json")
# Meta data score layer
df_score = (df['technique_id']
.value_counts()
.rename_axis('technique_id')
.reset_index(name='count')
.sort_values(by=['count'], ascending=True))
tl = []
max_score = df_score['count'].max().item() # Ensure the gradient is set correctly and return int
for index, row in df_score.iterrows():
#d['techniqueID'] = d.pop('technique_id')
techniques = {
"techniqueID": row['technique_id'],
"score": row['count'],
"color": "",
"comment": "",
"enabled": technique_enabled,
"metadata": [],
"showSubtechniques": show_tub_techniques
}
tl.append(techniques)
NAME = "CB Endpoint Standard: Analytic Alerts with Scoring"
write_layer(NAME, tl, f"{args.project}_attack_cb_metadata_score.json", max_score)
# Metadata score with sensor action layer
data = df[["technique_id","device_name","id","sensor_action"]].reset_index(drop=True).drop_duplicates()
data["id_sa"] = data['id'] + (' (' + data["sensor_action"] + ')').fillna('')
grouped = data.groupby(['technique_id'], as_index=False).agg({'id_sa': lambda x: x.tolist()})
score = []
for index, row in grouped.iterrows():
score.append(len(row['id_sa']))
grouped = grouped.assign(score = score)
grouped['id_sa'] = [',\n\n'.join(map(str, x)) for x in grouped['id_sa']]
tl = []
max_score = df_score['count'].max().item() # Ensure the gradient is set correctly and return int
for index, row in grouped.iterrows():
techniques = {
"techniqueID": row['technique_id'],
"score": row['score'],
"color": "",
"comment": "",
"enabled": technique_enabled,
"metadata": [
{
"name": "Alert ID",
"value": row['id_sa']
}
],
"showSubtechniques": show_tub_techniques
}
tl.append(techniques)
NAME = "CB Endpoint Standard: Analytic Alerts with Scoring and Sensor Action"
write_layer(NAME, tl, f"{args.project}_attack_cb_metadata_score_sensorAction.json", max_score)
# Metadata scoring alert count layer
data = df[["technique_id","device_name","id"]].reset_index(drop=True).drop_duplicates()
data = df[["technique_id","device_name","id"]].reset_index(drop=True)
grouped = data.groupby(['technique_id'], as_index=False).agg({'id': lambda x: x.tolist()})
score = []
for index, row in grouped.iterrows():
score.append(len(row['id']))
grouped = grouped.assign(score = score)
grouped['id'] = [',\n\n'.join(map(str, x)) for x in grouped['id']]
tl = []
max_score = df_score['count'].max().item() # Ensure the gradient is set correctly and return int
for index, row in grouped.iterrows():
techniques = {
"techniqueID": row['technique_id'],
"score": row['score'],
"color": "",
"comment": "",
"enabled": technique_enabled,
"metadata": [
{
"name": "Alert ID",
"value": row['id']
}
],
"showSubtechniques": show_tub_techniques
}
tl.append(techniques)
NAME = "CB Endpoint Standard: Analytic Alerts with scoring by alert count"
write_layer(NAME, tl, f'{args.project}_attack_cb_metadata_score_alert_count.json', max_score)
if __name__ == "__main__":
sys.exit(main())