analyzing-apt-group-with-mitre-navigator

使用 MITRE ATT&CK Navigator 分析 APT 组织

概述

MITRE ATT&CK Navigator 是一款基于 Web 的工具，用于标注和探索 ATT&CK 矩阵，使分析师能够可视化威胁行为者的技术覆盖范围、比较多个 APT 组织、识别检测缺口，并构建基于威胁情报的防御策略。本技能涵盖以编程方式查询 ATT&CK 数据、将 APT 组织 TTP 映射到 Navigator 图层、创建用于差距分析的多图层叠加，以及为检测工程团队生成可操作的情报报告。

何时使用

• 当调查安全事件需要借助 MITRE Navigator 分析 APT 组织时
• 当为该领域构建检测规则或威胁狩猎查询时
• 当 SOC 分析师需要针对此类分析的结构化流程时
• 当验证相关攻击技术的安全监控覆盖范围时

前提条件

• Python 3.9+，并安装 attackcti、mitreattack-python、stix2、requests 库
• ATT&CK Navigator（https://mitre-attack.github.io/attack-navigator/）或本地部署
• 了解 ATT&CK Enterprise 矩阵：14 个战术、200+ 个技术、子技术
• 可访问威胁情报报告或 MISP/OpenCTI 以获取威胁行为者数据
• 熟悉 STIX 2.1 Intrusion Set 和 Attack Pattern 对象

关键概念

ATT&CK Navigator 图层

Navigator 图层是 JSON 文件，用于为 ATT&CK 技术标注分数、颜色、注释和元数据。每个图层可以表示单个 APT 组织的技术使用情况、检测能力映射，或组合叠加。图层版本 4.5 支持 enterprise-attack、mobile-attack 和 ics-attack 域，并可按平台（Windows、Linux、macOS、Cloud、Azure AD、Office 365、SaaS）进行筛选。

ATT&CK 中的 APT 组织画像

ATT&CK 收录了 140 多个威胁组织及其有记录的技術使用情况。每个组织画像包括别名、目标行业、相关行动、使用的软件，以及带有过程级细节的技术映射。组织由 G 编号标识（例如，APT29 为 G0016，APT28 为 G0007，Lazarus Group 为 G0032）。

多图层分析

Navigator 支持同时加载多个图层，使分析师能够将威胁行为者 TTP 与检测覆盖范围叠加以识别缺口、比较多个 APT 组织以找到值得优先处理的共同技术，并跟踪技术覆盖范围随时间的变化。

工作流

步骤 1：查询 APT 组织的 ATT&CK 数据

from attackcti import attack_client
import json

lift = attack_client()

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

# Find APT29 (Cozy Bear / Midnight Blizzard)
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]}")

# Get techniques used by APT29 (G0016)
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", []),
        }

步骤 2：生成 Navigator 图层 JSON

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")

步骤 3：比较多个 APT 组织

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")

步骤 4：使用图层叠加进行检测缺口分析

# Define your current detection capabilities
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"=== 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}%)")

# Create gap layer (red = undetected, green = detected)
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"{'DETECTED' 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 Detection 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": "Detection Gap", "color": "#ff3333"},
    ],
}
with open("apt29_gap_layer.json", "w") as f:
    json.dump(gap_layer, f, indent=2)

步骤 5：战术细分分析

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']}")

验证标准

• 通过 TAXII 服务器成功查询 ATT&CK 数据
• APT 组织映射到所有已记录的技术，并附有过程示例
• Navigator 图层 JSON 可通过验证，并在 ATT&CK Navigator 中正确渲染
• 多图层叠加显示威胁行为者与检测覆盖范围的对比
• 检测缺口分析识别出未监控的技术，并给出数据源建议
• 跨组织比较揭示共有的和独有的 TTP
• 输出可操作，便于检测工程确定优先级

参考资料

• MITRE ATT&CK Navigator
• ATT&CK Groups
• attackcti Python Library
• Navigator Layer Format v4.5
• CISA Best Practices for MITRE ATT&CK Mapping
• Picus: Leverage MITRE ATT&CK for Threat Intelligence