网络安全 Analyzing Threat Actor Ttps With Mitre Attack

Analyzing Threat Actor Ttps With Mitre Attack：网络安全 skill: analyzing-threat-actor-ttps-with-mitre，适用于安全分析、取证与威胁排查场景。

Skill Documentation

网络安全 Analyzing Threat Actor Ttps With Mitre Attack

摘要

Analyzing Threat Actor Ttps With Mitre Attack：网络安全 skill: analyzing-threat-actor-ttps-with-mitre，适用于安全分析、取证与威胁排查场景。

> 来源: mukul975/Anthropic-Cybersecurity-Skills (18k stars) — 网络安全专业技能集

> 原文件: skills/analyzing-threat-actor-ttps-with-mitre-attack/SKILL.md

> 模型推荐: claude-opus-4-7 (安全分析深度推理)

这个 skill 是干嘛的

mukul975 整理的 100+ 个网络安全专业 skill — 覆盖渗透测试 / 取证 / 威胁情报 / 合规审计 / 云安全 / 移动安全 等领域。每个 skill 对应一个具体的安全分析任务。

michael 强调"skill 要有相应的指导功能,指导用户使用",所以加了下面两节让 Agent 和用户对接。

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🤖 Agent 使用说明

1. 用户提到"分析 X 日志 / 取证 / 检测威胁 / 渗透测试 / 安全审计"时触发对应 skill

2. skill 按操作步骤执行(取证镜像 / 解析日志 / 跑威胁情报)

3. 涉及破坏性操作前必须 ask user 确认

4. 完工后跑自检

5. 区分"防御性分析" vs "恶意代码审计"

👤 用户需要做什么?

1. 告诉 Agent 你要做什么(分析日志 / 取证 / 安全审计 / 渗透测试)

2. 按 Agent 提示提供文件/镜像/日志/哈希

3. 涉及破坏性操作时明确告诉 Agent"继续"或"取消"

4. 全程 Agent 自动化,你只需提供数据 + 回答决策点

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原 skill 内容(mukul975/Anthropic-Cybersecurity-Skills/skills/analyzing-threat-actor-ttps-with-mitre-attack/SKILL.md,截断到 12k chars)

---

name: analyzing-threat-actor-ttps-with-mitre-attack

description: Systematically map threat actor behavior and observed IOCs to the MITRE ATT&CK framework, build technique coverage heatmaps with the ATT&CK Navigator, identify detection gaps, and produce actionable threat intelligence reports across the Enterprise, Mobile, and ICS matrices. Use when analyzing threat actor TTPs, correlating IOCs to specific ATT&CK techniques, or assessing defensive detection coverage against adversary behavior.

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

mitre_attack:

• T1566.001
• T1059.001
• T1071.001
• T1547.001
• T1053.005

---

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

# 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

References

• [MITRE ATT&CK](https://attack.mitre.org/)
• [ATT&CK Navigator](https://mitre-attack.github.io/attack-navigator/)
• [attackcti Python Library](https://github.com/OTRF/ATTACK-Python-Client)
• [ATT&CK STIX Data](https://github.com/mitre/cti)
• [ATT&CK Groups](https://attack.mitre.org/groups/)

常见问题（FAQ）

使用「Analyzing Thre」这个 skill 能解决什么问题?

本 skill 专注于Analyzing Thre，网络安全 skill: analyzing-threat-actor-ttps-with-mitre-attack。它将相关流程标准化，帮助用户更快拿到可靠结果，减少重复手工操作。

什么情况下适合使用「Analyzing Thre」?

当你需要在Analyzing Threat Actor Ttps With Mitre Attack相关工作中获得稳定、可复用的产出时最适合——无论是单次任务还是纳入日常工作流，都能直接调用。

使用「Analyzing Thre」前需要准备什么?

需要明确授权范围内的目标系统或样本文件，并准备隔离的分析环境（虚拟机/沙箱）。