undermybelt Analyzing Threat Actor TTPs with MITRE ATT&CK
Overview
MITRE ATT&CK is a globally-accessible knowledge base of adversary tactics, techniques, and procedures (TTPs) based on real-world observations. This skill covers systematically mapping threat actor behavior to the ATT&CK framework, building technique coverage heatmaps using the ATT&CK Navigator, identifying detection gaps, and producing actionable intelligence reports that link observed IOCs to specific adversary techniques across the Enterprise, Mobile, and ICS matrices.
When to Use
- When investigating security incidents that require analyzing threat actor ttps with mitre attack
- When building detection rules or threat hunting queries for this domain
- When SOC analysts need structured procedures for this analysis type
- When validating security monitoring coverage for related attack techniques
Prerequisites
- Python 3.9+ with
mitreattack-python,attackcti,stix2libraries - MITRE ATT&CK Navigator (web-based or local deployment)
- Understanding of ATT&CK matrix structure: Tactics, Techniques, Sub-techniques
- Access to threat intelligence reports or MISP/OpenCTI for threat actor data
- Familiarity with STIX 2.1 Attack Pattern objects
Key Concepts
ATT&CK Matrix Structure
The ATT&CK Enterprise matrix organizes adversary behavior into 14 Tactics (the "why") containing Techniques (the "how") and Sub-techniques (specific implementations). Each technique has associated data sources, detections, mitigations, and real-world procedure examples from observed threat groups.
Threat Group Profiles
ATT&CK catalogs over 140 threat groups (e.g., APT28, APT29, Lazarus Group, FIN7) with documented technique usage. Each group profile includes aliases, targeted sectors, associated campaigns, software used, and technique mappings with procedure-level detail.
ATT&CK Navigator
The ATT&CK Navigator is a web-based tool for creating custom ATT&CK matrix visualizations. Analysts create layers (JSON files) that annotate techniques with scores, colors, comments, and metadata to visualize threat actor coverage, detection capabilities, or risk assessments.
Workflow
Step 1: Query ATT&CK Data Programmatically
from attackcti import attack_client
import json
# Initialize ATT&CK client (queries MITRE TAXII server)
lift = attack_client()
# Get all Enterprise techniques
enterprise_techniques = lift.get_enterprise_techniques()
print(f"Total Enterprise techniques: {len(enterprise_techniques)}")
# Get all threat groups
groups = lift.get_groups()
print(f"Total threat groups: {len(groups)}")
# Get specific group by name
apt29 = [g for g in groups if 'APT29' in g.get('name', '')]
if apt29:
group = apt29[0]
print(f"Group: {group['name']}")
print(f"Aliases: {group.get('aliases', [])}")
print(f"Description: {group.get('description', '')[:200]}")
Step 2: Map Threat Actor to ATT&CK Techniques
from attackcti import attack_client
lift = attack_client()
# Get techniques used by APT29
apt29_techniques = lift.get_techniques_used_by_group("G0016") # APT29 group ID
technique_map = {}
for entry in apt29_techniques:
tech_id = entry.get("external_references", [{}])[0].get("external_id", "")
tech_name = entry.get("name", "")
description = entry.get("description", "")
tactic_refs = [
phase.get("phase_name", "")
for phase in entry.get("kill_chain_phases", [])
]
technique_map[tech_id] = {
"name": tech_name,
"tactics": tactic_refs,
"description": description[:300],
}
print(f"\nAPT29 uses {len(technique_map)} techniques:")
for tid, info in sorted(technique_map.items()):
print(f" {tid}: {info['name']} [{', '.join(info['tactics'])}]")
Step 3: Generate ATT&CK Navigator Layer
import json
def create_navigator_layer(group_name, technique_map, description=""):
"""Generate ATT&CK Navigator layer JSON for a threat group."""
techniques_list = []
for tech_id, info in technique_map.items():
techniques_list.append({
"techniqueID": tech_id,
"tactic": info["tactics"][0] if info["tactics"] else "",
"color": "#ff6666", # Red for observed techniques
"comment": info["description"][:200],
"enabled": True,
"score": 100,
"metadata": [
{"name": "group", "value": group_name},
],
})
layer = {
"name": f"{group_name} TTP Coverage",
"versions": {
"attack": "16.1",
"navigator": "5.1.0",
"layer": "4.5",
},
"domain": "enterprise-attack",
"description": description or f"Techniques attributed to {group_name}",
"filters": {"platforms": ["Windows", "Linux", "macOS", "Cloud"]},
"sorting": 0,
"layout": {
"layout": "side",
"aggregateFunction": "average",
"showID": True,
"showName": True,
"showAggregateScores": False,
"countUnscored": False,
},
"hideDisabled": False,
"techniques": techniques_list,
"gradient": {
"colors": ["#ffffff", "#ff6666"],
"minValue": 0,
"maxValue": 100,
},
"legendItems": [
{"label": "Observed technique", "color": "#ff6666"},
{"label": "Not observed", "color": "#ffffff"},
],
"showTacticRowBackground": True,
"tacticRowBackground": "#dddddd",
"selectTechniquesAcrossTactics": True,
"selectSubtechniquesWithParent": False,
"selectVisibleTechniques": False,
}
return layer
# Generate and save layer
layer = create_navigator_layer("APT29", technique_map, "APT29 (Cozy Bear) TTP analysis")
with open("apt29_navigator_layer.json", "w") as f:
json.dump(layer, f, indent=2)
print("[+] Navigator layer saved to apt29_navigator_layer.json")
Step 4: Identify Detection Gaps
from attackcti import attack_client
lift = attack_client()
# Get all techniques with data sources
all_techniques = lift.get_enterprise_techniques()
# Build data source coverage map
data_source_coverage = {}
for tech in all_techniques:
tech_id = tech.get("external_references", [{}])[0].get("external_id", "")
data_sources = tech.get("x_mitre_data_sources", [])
for ds in data_sources:
if ds not in data_source_coverage:
data_source_coverage[ds] = []
data_source_coverage[ds].append(tech_id)
# Compare threat actor techniques against available detections
detected_techniques = {"T1059", "T1071", "T1566"} # Example: techniques you can detect
actor_techniques = set(technique_map.keys())
covered = actor_techniques.intersection(detected_techniques)
gaps = actor_techniques - detected_techniques
print(f"\n=== Detection Gap Analysis for APT29 ===")
print(f"Actor techniques: {len(actor_techniques)}")
print(f"Detected: {len(covered)} ({len(covered)/len(actor_techniques)*100:.0f}%)")
print(f"Gaps: {len(gaps)} ({len(gaps)/len(actor_techniques)*100:.0f}%)")
print(f"\nUndetected techniques:")
for tech_id in sorted(gaps):
if tech_id in technique_map:
print(f" {tech_id}: {technique_map[tech_id]['name']}")
Step 5: Cross-Group Technique Comparison
from attackcti import attack_client
lift = attack_client()
# Compare techniques across multiple groups
groups_to_compare = {
"G0016": "APT29",
"G0007": "APT28",
"G0032": "Lazarus Group",
}
group_techniques = {}
for gid, gname in groups_to_compare.items():
techs = lift.get_techniques_used_by_group(gid)
tech_ids = set()
for t in techs:
tid = t.get("external_references", [{}])[0].get("external_id", "")
if tid:
tech_ids.add(tid)
group_techniques[gname] = tech_ids
# Find common and unique techniques
all_groups = list(group_techniques.keys())
common_to_all = set.intersection(*group_techniques.values())
print(f"\nTechniques common to all {len(all_groups)} groups: {len(common_to_all)}")
for tid in sorted(common_to_all):
print(f" {tid}")
for gname, techs in group_techniques.items():
unique = techs - set.union(*[t for n, t in group_techniques.items() if n != gname])
print(f"\nUnique to {gname}: {len(unique)} techniques")
Validation Criteria
- ATT&CK data successfully queried via TAXII server or local copy
- Threat actor mapped to specific techniques with procedure examples
- ATT&CK Navigator layer JSON is valid and renders correctly
- Detection gap analysis identifies unmonitored techniques
- Cross-group comparison reveals shared and unique TTPs
- Output is actionable for detection engineering prioritization