| name | analyzing-threat-actor-ttps-with-mitre-attack |
| description | 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 beh |
| domain | cybersecurity |
| subdomain | threat-intelligence |
| tags | - threat-intelligence - cti - ioc - mitre-attack - stix - ttp-analysis - threat-actors |
| version | '1.0' |
| author | mahipal |
| license | Apache-2.0 |
| d3fend_techniques | - Executable Denylisting - Execution Isolation - File Metadata Consistency Validation - Content Format Conversion - File Content Analysis |
| nist_csf | - ID.RA-01 - ID.RA-05 - DE.CM-01 - DE.AE-02 |
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, stix2 libraries
- 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
lift = attack_client()
enterprise_techniques = lift.get_enterprise_techniques()
print(f"Total Enterprise techniques: {len(enterprise_techniques)}")
groups = lift.get_groups()
print(f"Total threat groups: {len(groups)}")
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()
apt29_techniques = lift.get_techniques_used_by_group("G0016")
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",
"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
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()
all_techniques = lift.get_enterprise_techniques()
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)
detected_techniques = {"T1059", "T1071", "T1566"}
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()
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
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
References
