使用 MITRE ATT&CK Navigator 分析 APT 组织

使用 MITRE ATT&CK Navigator 分析 APT 组织 是做什么的？

概述

MITRE ATT&CK Navigator 是一个基于 Web 的工具，用于注释和探索 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、识别检测差距，并构建威胁驱动的防御策略。本技能涵盖如何以编程方式查询 ATT&CK 数据、将 APT 组织的 TTP（战术、技术和过程）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

• 在调查需要分析 APT 组织及 MITRE Navigator 的安全事件时
• 在为此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标准化流程时
• 在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 企业矩阵：14 个战术、200+ 技术、子技术
• 能够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 的入侵集合和攻击模式对象

关键概念

ATT&CK Navigator 图层

Navigator 图层是 JSON 文件，用于对 ATT&CK 技术进行评分、着色、添加注释和元数据标注。每个图层可以代表单个 APT 组织的技术使用情况、检测能力地图或组合叠加图。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）进行过滤。

ATT&CK 中的 APT 组织简介

ATT&CK 收录了 140 多个威胁组织及其记录在案的技术使用情况。每个组织简介包括别名、目标行业、相关活动、使用的软件以及带有过程级细节的技术映射。组织通过 G 代码标识（例如，G0016 对应 APT29，G0007 对应 APT28，G0032 对应 Lazarus Group）。

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发现差距、比较多个 APT 组织以找出值得优先关注的通用技术、以及追踪随时间变化的技术覆盖率。

工作流程

第一步：为 APT 组织查询 ATT&CK 数据

from attackcti import attack_client
import json

lift = attack_client()

# 获取所有威胁组织
groups = lift.get_groups()
print(f"总 ATT&CK 组织数: {len(groups)}")

# 查找 APT29 (Cozy Bear / Midnight Blizzard)
apt29 = next((g for g in groups if g.get('name') == 'APT29'), None)
if apt29:
    print(f"组织: {apt29['name']}")
    print(f"别名: {apt29.get('aliases', [])}")
    print(f"描述: {apt29.get('description', '')[:300]}")

# 获取 APT29 (G0016) 使用的技术
techniques = lift.get_techniques_used_by_group("G0016")
print(f"APT29 使用了 {len(techniques)} 项技术")

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", []),
        }

第二步：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 覆盖范围",
        "versions": {"attack": "16.1", "navigator": "5.1.0", "layer": "4.5"},
        "domain": "enterprise-attack",
        "description": f"归因于 {group_name} 的技术",
        "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"由 {group_name} 使用", "color": color},
            {"label": "未观察到", "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("[+] 图层已保存: apt29_layer.json")

第三步：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"所有组织共有的技术数量: {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"\n{gname} 独有的技术: {len(unique)} 项")

第四步：图层叠加的检测差距分析

# 定义您当前的检测能力
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"=== APT29 检测差距分析 ===")
print(f"行为者技术数: {len(actor_techniques)}")
print(f"已检测到: {len(covered)} ({len(covered)/len(actor_techniques)*100:.0f}%)")
print(f"检测差距: {len(gaps)} ({len(gaps)/len(actor_techniques)*100:.0f}%)")

# 创建差距图层（红色=未检测到，绿色=已检测到）
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"{'已检测' if tech_id in detected_techniques else '差距'}: {info.get('name', '')}",
            "enabled": True,
            "score": 100 if tech_id in detected_techniques else 0,
        })

gap_layer = {
    "name": "APT29 检测差距分析",
    "versions": {"attack": "16.1", "navigator": "5.1.0", "layer": "4.5"},
    "domain": "enterprise-attack",
    "description": "绿色 = 已检测，红色 = 差距",
    "techniques": gap_techniques,
    "gradient": {"colors": ["#ff3333", "#66ff66"], "minValue": 0, "maxValue": 100},
    "legendItems": [
        {"label": "已检测", "color": "#66ff66"},
        {"label": "检测差距", "color": "#ff3333"},
    ],
}
with open("apt29_gap_layer.json", "w") as f:
    json.dump(gap_layer, f, indent=2)

第五步：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 战术细分 ===")
for tactic in tactic_order:
    techs = tactic_breakdown.get(tactic, [])
    if techs:
        print(f"\n{tactic.upper()} ({len(techs)} 项技术):")
        for t in techs:
            print(f"  {t['id']}: {t['name']}")

验证标准

• ATT&CK 数据已通过 TAXII 服务器成功查询
• APT 组织已映射到所有已记录的技术（含过程示例）
• Navigator 图层 JSON 验证通过，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
• 检测差距分析识别出未监控的技术，并附带数据源建议
• 跨组织比较揭示共享和独有的 TTP
• 输出结果对检测工程优先级排序具有可操作性

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