Analyzing Apt Group With Mitre Navigator

---
name: analyzing-apt-group-with-mitre-navigator
description: Analyze advanced persistent threat (APT) group techniques using MITRE ATT&CK Navigator to create layered heatmaps of adversary TTPs for Tespit gap analysis and threat-informed defense.
tags:
- ttp-analysis
- navigator
- threat-intelligence
- Tespit-gap
- threat-actor
- heatmap
- fetih
- mitre-attack
- cybersecurity
- siber-güvenlik
- apt
triggers:
- IOC
- analyzing
- cloud
- group
- http
- incident
- indicator of compromise
- log
- mitre
- mobile
- navigator
- tehdit aktörü
category: threat-intelligence
source_subdomain: threat-intelligence
nist_csf:
- ID.RA-01
- ID.RA-05
- DE.CM-01
- DE.AE-02
adapted_for: fetih
---

# Analyzing Apt Group with Mitre Navigator


## Genel Bakış

MITRE ATT&CK Navigator is a web-based tool for annotating and exploring ATT&CK matrices, enabling analysts to visualize threat actor technique coverage, compare multiple APT groups, identify Tespit gaps, and build threat-informed defense strategies. bu skill covers querying ATT&CK data programmatically, mapping APT group TTPs to Navigator layers, creating multi-layer overlays for gap analysis, and generating actionable intelligence reports for Tespit engineering teams.


## Ne Zaman Kullanılır

- investigating yaparken security incidents that require analyzing apt group with mitre navigator
- building yaparken Tespit rules or threat hunting queries for this domain
- SOC yaparken: analysts need structured procedures for this analysis type
- validating yaparken security monitoring coverage for related attack techniques

## Ön Gereksinimler

- Python 3.9+ with `attackcti`, `mitreattack-python`, `stix2`, `requests` libraries
- ATT&CK Navigator (https://mitre-attack.github.io/attack-navigator/) or local Dağıt:ment
- Understanding of ATT&CK Enterprise matrix: 14 Tactics, 200+ Techniques, Sub-techniques
- Erişim: threat intelligence reports or MISP/OpenCTI for threat actor data
- Familiarity with STIX 2.1 Intrusion Set and Attack Pattern objects

## Key Concepts

### ATT&CK Navigator Layers

Navigator layers are JSON files that annotate ATT&CK techniques with scores, colors, comments, and metadata. Each layer can represent a single APT group's technique usage, a Tespit capability map, or a combined overlay. Layer version 4.5 supports enterprise-attack, mobile-attack, and ics-attack domains with filtering by platform (Windows, Linux, macOS, Cloud, Azure AD, Office 365, SaaS).

### APT Group Profiles in ATT&CK

ATT&CK catalogs over 140 threat groups with documented technique usage. Each group profile includes aliases, targeted sectors, associated campaigns, software used, and technique mappings with procedure-level detail. Groups are identified by G-codes (e.g., G0016 for APT29, G0007 for APT28, G0032 for Lazarus Group).

### Multi-Layer Analysis

The Navigator supports loading multiple layers simultaneously, allowing analysts to overlay threat actor TTPs against Tespit coverage to identify gaps, compare multiple APT groups to Bul: common techniques worth prioritizing, and track technique coverage changes over time.

## İş Akışı

### Adım 1: Query ATT&CK Data for APT Group

```python
from attackcti import attack_client
import json

lift = attack_client()

groups = lift.get_groups()
print(f"Total ATT&CK groups: {len(groups)}")

apt29 = next((g for g in groups if g.get('name') == 'APT29'), None)
if apt29:
    print(f"Group: {apt29['name']}")
    print(f"Aliases: {apt29.get('aliases', [])}")
    print(f"Description: {apt29.get('description', '')[:300]}")

techniques = lift.get_techniques_used_by_group("G0016")
print(f"APT29 uses {len(techniques)} techniques")

technique_map = {}
for tech in techniques:
    tech_id = ""
    for ref in tech.get("external_references", []):
        if ref.get("source_name") == "mitre-attack":
            tech_id = ref.get("external_id", "")
            break
    if tech_id:
        tactics = [p.get("phase_name", "") for p in tech.get("kill_chain_phases", [])]
        technique_map[tech_id] = {
            "name": tech.get("name", ""),
            "tactics": tactics,
            "description": tech.get("description", "")[:500],
            "platforms": tech.get("x_mitre_platforms", []),
            "data_sources": tech.get("x_mitre_data_sources", []),
        }
```

### Adım 2: Generate Navigator Layer JSON

```python
def create_navigator_layer(group_name, technique_map, color="#ff6666"):
    techniques_list = []
    for tech_id, info in technique_map.items():
        for tactic in info["tactics"]:
            techniques_list.append({
                "techniqueID": tech_id,
                "tactic": tactic,
                "color": color,
                "comment": info["name"],
                "enabled": True,
                "score": 100,
                "metadata": [
                    {"name": "group", "value": group_name},
                    {"name": "platforms", "value": ", ".join(info["platforms"])},
                ],
            })

    layer = {
        "name": f"{group_name} TTP Coverage",
        "versions": {"attack": "16.1", "navigator": "5.1.0", "layer": "4.5"},
        "domain": "enterprise-attack",
        "description": f"Techniques attributed to {group_name}",
        "filters": {
            "platforms": ["Linux", "macOS", "Windows", "Cloud",
                          "Azure AD", "Office 365", "SaaS", "Google Workspace"]
        },
        "sorting": 0,
        "layout": {
            "layout": "side", "aggregateFunction": "average",
            "showID": True, "showName": True,
            "showAggregateScores": False, "countUnscored": False,
        },
        "hideDisabled": False,
        "techniques": techniques_list,
        "gradient": {"colors": ["#ffffff", color], "minValue": 0, "maxValue": 100},
        "legendItems": [
            {"label": f"Used by {group_name}", "color": color},
            {"label": "Not observed", "color": "#ffffff"},
        ],
        "showTacticRowBackground": True,
        "tacticRowBackground": "#dddddd",
        "selectTechniquesAcrossTactics": True,
        "selectSubtechniquesWithParent": False,
        "selectVisibleTechniques": False,
    }
    return layer

layer = create_navigator_layer("APT29", technique_map)
with open("apt29_layer.json", "w") as f:
    json.dump(layer, f, indent=2)
print("[+] Layer saved: apt29_layer.json")
```

### Adım 3: Compare Multiple APT Groups

```python
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:
        for ref in t.get("external_references", []):
            if ref.get("source_name") == "mitre-attack":
                tech_ids.add(ref.get("external_id", ""))
    group_techniques[gname] = tech_ids

common_to_all = set.intersection(*group_techniques.values())
print(f"Techniques common to all groups: {len(common_to_all)}")
for tid in sorted(common_to_all):
    print(f"  {tid}")

for gname, techs in group_techniques.items():
    others = set.union(*[t for n, t in group_techniques.items() if n != gname])
    unique = techs - others
    print(f"\nUnique to {gname}: {len(unique)} techniques")
```

### Adım 4: Tespit Gap Analysis with Layer Overlay

```python
Detected_techniques = {
    "T1059", "T1059.001", "T1071", "T1071.001", "T1566", "T1566.001",
    "T1547", "T1547.001", "T1053", "T1053.005", "T1078", "T1027",
}

actor_techniques = set(technique_map.keys())
covered = actor_techniques.intersection(Detected_techniques)
gaps = actor_techniques - Detected_techniques

print(f"=== Tespit 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}%)")

gap_techniques = []
for tech_id in actor_techniques:
    info = technique_map.get(tech_id, {})
    for tactic in info.get("tactics", [""]):
        color = "#66ff66" if tech_id in Detected_techniques else "#ff3333"
        gap_techniques.append({
            "techniqueID": tech_id,
            "tactic": tactic,
            "color": color,
            "comment": f"{'tespit etme (ED)' if tech_id in Detected_techniques else 'GAP'}: {info.get('name', '')}",
            "enabled": True,
            "score": 100 if tech_id in Detected_techniques else 0,
        })

gap_layer = {
    "name": "APT29 Tespit Gap Analysis",
    "versions": {"attack": "16.1", "navigator": "5.1.0", "layer": "4.5"},
    "domain": "enterprise-attack",
    "description": "Green = Detected, Red = gap",
    "techniques": gap_techniques,
    "gradient": {"colors": ["#ff3333", "#66ff66"], "minValue": 0, "maxValue": 100},
    "legendItems": [
        {"label": "Detected", "color": "#66ff66"},
        {"label": "Tespit Gap", "color": "#ff3333"},
    ],
}
with open("apt29_gap_layer.json", "w") as f:
    json.dump(gap_layer, f, indent=2)
```

### Adım 5: Tactic Breakdown Analysis

```python
from collections import defaultdict

tactic_breakdown = defaultdict(list)
for tech_id, info in technique_map.items():
    for tactic in info["tactics"]:
        tactic_breakdown[tactic].append({"id": tech_id, "name": info["name"]})

tactic_order = [
    "reconnaissance", "resource-development", "initial-access",
    "execution", "persistence", "privilege-escalation",
    "defense-evasion", "credential-access", "discovery",
    "lateral-movement", "collection", "command-and-control",
    "exfiltration", "impact",
]

print("\n=== APT29 Tactic Breakdown ===")
for tactic in tactic_order:
    techs = tactic_breakdown.get(tactic, [])
    if techs:
        print(f"\n{tactic.upper()} ({len(techs)} techniques):")
        for t in techs:
            print(f"  {t['id']}: {t['name']}")
```

## Doğrulama Criteria

- ATT&CK data queried successfully via TAXII server
- APT group mapped to all documented techniques with procedure examples
- Navigator layer JSON validates and renders correctly in ATT&CK Navigator
- Multi-layer overlay shows threat actor vs. Tespit coverage
- Tespit gap analysis identifies unmonitored techniques with data source recommendations
- Cross-group comparison reveals shared and unique TTPs
- Output is actionable for Tespit engineering prioritization

## References

- [MITRE ATT&CK Navigator](https://mitre-attack.github.io/attack-navigator/)
- [ATT&CK Groups](https://attack.mitre.org/groups/)
- [attackcti Python Library](https://github.com/OTRF/ATTACK-Python-Client)
- [Navigator Layer Format v4.5](https://github.com/mitre-attack/attack-navigator/blob/master/layers/LAYERFORMATv4_5.md)
- [CISA Best Practices for MITRE ATT&CK Mapping](https://www.cisa.gov/sites/default/files/2023-01/Best%20Practices%20for%20MITRE%20ATTCK%20Mapping.pdf)
- [Picus: Leverage MITRE ATT&CK for Threat Intelligence](https://www.picussecurity.com/how-to-leverage-the-mitre-attack-framework-for-threat-intelligence)

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