analyzing-ransomware-leak-site-intelligence

# 分析勒索软件数据泄露站点情报

## 概述

采用双重勒索模式运营的勒索软件（Ransomware）组织在 Tor 隐藏服务上维护数据泄露站点（DLS），在那里发布受害者名称、被盗数据样本和倒计时器以施压付款。2025 年上半年，96 个独特勒索软件组织活跃，每月约发布 535 名受害者。监控这些站点提供了关于活跃威胁组织、目标行业、地理模式和新兴勒索软件家族的情报。本技能涵盖安全收集 DLS 情报、提取结构化数据、追踪组织活动趋势，以及生成行业特定风险评估。

## 前置条件

- Python 3.9+，安装 `requests`、`beautifulsoup4`、`pandas`、`matplotlib` 库
- Tor 代理（SOCKS5）用于访问 .onion 站点，或商业 DLS 监控情报
- 了解勒索软件双重勒索商业模式
- 熟悉主要勒索软件家族（Qilin、Akira、LockBit、BlackCat、Clop）
- 访问勒索软件追踪情报（Ransomwatch、RansomLook、DarkFeed）

## 核心概念

### 双重勒索模式

现代勒索软件组织在加密受害者数据之前还会将其外泄（Exfiltration）。泄露站点作为公开施压工具：受害者以倒计时器、部分数据样本和文件目录的形式被列出。若未支付赎金，完整数据将被公开。部分组织已转向三重勒索，追加 DDoS 威胁或直接联系受害者客户。

### DLS 情报价值

泄露站点提供：受害者识别（公司名称、行业、国家）、攻击时间线（列出时间、截止日期、数据发布时间）、数据量估算、组织能力评估（目标行业、攻击频率、操作节奏），以及趋势分析（新组织出现、组织品牌重塑、执法打击）。

### 安全收集实践

切勿在生产环境中直接访问 DLS 站点。使用专用监控服务（Ransomwatch、DarkFeed、KELA、Flashpoint）、Tor 隔离研究虚拟机、商业威胁情报平台或社区维护的数据集。所有分析应在隔离环境中进行，并获得适当授权。

## 实践步骤

### 步骤 1：从公开情报源导入勒索软件泄露站点数据

```python
import requests
import json
import pandas as pd
from datetime import datetime, timedelta
from collections import Counter

class RansomwareIntelCollector:
    """从公开追踪来源收集勒索软件 DLS 情报。"""

    RANSOMWATCH_API = "https://raw.githubusercontent.com/joshhighet/ransomwatch/main/posts.json"
    RANSOMWATCH_GROUPS = "https://raw.githubusercontent.com/joshhighet/ransomwatch/main/groups.json"

    def __init__(self):
        self.posts = []
        self.groups = []

    def fetch_ransomwatch_data(self):
        """从 ransomwatch 获取勒索软件受害者发布数据。"""
        resp = requests.get(self.RANSOMWATCH_API, timeout=30)
        if resp.status_code == 200:
            self.posts = resp.json()
            print(f"[+] 已从 ransomwatch 加载 {len(self.posts)} 条受害者记录")
        else:
            print(f"[-] 获取记录失败: {resp.status_code}")

        resp = requests.get(self.RANSOMWATCH_GROUPS, timeout=30)
        if resp.status_code == 200:
            self.groups = resp.json()
            print(f"[+] 已加载 {len(self.groups)} 个勒索软件组织画像")

        return self.posts

    def get_recent_victims(self, days=30):
        """获取最近 N 天内发布的受害者。"""
        cutoff = datetime.now() - timedelta(days=days)
        recent = []
        for post in self.posts:
            try:
                discovered = datetime.fromisoformat(
                    post.get("discovered", "").replace("Z", "+00:00")
                )
                if discovered.replace(tzinfo=None) >= cutoff:
                    recent.append(post)
            except (ValueError, TypeError):
                continue
        print(f"[+] 最近 {days} 天内 {len(recent)} 名受害者")
        return recent

    def get_group_activity(self, group_name):
        """获取特定勒索软件组织的所有发布记录。"""
        group_posts = [
            p for p in self.posts
            if p.get("group_name", "").lower() == group_name.lower()
        ]
        print(f"[+] {group_name}: 共 {len(group_posts)} 名受害者")
        return group_posts

collector = RansomwareIntelCollector()
collector.fetch_ransomwatch_data()
recent = collector.get_recent_victims(days=30)
```

### 步骤 2：分析组织活动和趋势

```python
def analyze_group_trends(posts, top_n=15):
    """分析勒索软件组织活动趋势。"""
    group_counts = Counter(p.get("group_name", "unknown") for p in posts)
    monthly_activity = {}

    for post in posts:
        try:
            date = datetime.fromisoformat(
                post.get("discovered", "").replace("Z", "+00:00")
            )
            month_key = date.strftime("%Y-%m")
            group = post.get("group_name", "unknown")
            if month_key not in monthly_activity:
                monthly_activity[month_key] = Counter()
            monthly_activity[month_key][group] += 1
        except (ValueError, TypeError):
            continue

    analysis = {
        "total_posts": len(posts),
        "unique_groups": len(group_counts),
        "top_groups": group_counts.most_common(top_n),
        "monthly_totals": {
            month: sum(counts.values())
            for month, counts in sorted(monthly_activity.items())
        },
        "monthly_top_groups": {
            month: counts.most_common(5)
            for month, counts in sorted(monthly_activity.items())
        },
    }

    print(f"\n=== 勒索软件组织活动 ===")
    print(f"追踪受害者总数: {analysis['total_posts']}")
    print(f"活跃组织数量: {analysis['unique_groups']}")
    print(f"\n前 {top_n} 活跃组织:")
    for group, count in analysis["top_groups"]:
        print(f"  {group}: {count} 名受害者")

    return analysis

trends = analyze_group_trends(collector.posts)
```

### 步骤 3：行业和地理风险评估

```python
def assess_sector_risk(posts, target_sector=None, target_country=None):
    """评估特定行业或地区的勒索软件风险。"""
    sector_data = {}
    country_data = {}

    for post in posts:
        # 提取行业（并非所有情报源都包含此字段）
        sector = post.get("sector", post.get("industry", "unknown"))
        country = post.get("country", "unknown")

        if sector not in sector_data:
            sector_data[sector] = {"count": 0, "groups": Counter(), "recent": []}
        sector_data[sector]["count"] += 1
        sector_data[sector]["groups"][post.get("group_name", "")] += 1

        if country not in country_data:
            country_data[country] = {"count": 0, "groups": Counter()}
        country_data[country]["count"] += 1
        country_data[country]["groups"][post.get("group_name", "")] += 1

    # 行业风险评分
    total = len(posts)
    risk_assessment = {
        "total_victims": total,
        "sectors": {},
        "countries": {},
    }

    for sector, data in sorted(sector_data.items(), key=lambda x: -x[1]["count"]):
        pct = (data["count"] / total * 100) if total > 0 else 0
        risk_assessment["sectors"][sector] = {
            "victim_count": data["count"],
            "percentage": round(pct, 1),
            "top_groups": data["groups"].most_common(5),
            "risk_level": (
                "critical" if pct > 15
                else "high" if pct > 8
                else "medium" if pct > 3
                else "low"
            ),
        }

    for country, data in sorted(country_data.items(), key=lambda x: -x[1]["count"]):
        pct = (data["count"] / total * 100) if total > 0 else 0
        risk_assessment["countries"][country] = {
            "victim_count": data["count"],
            "percentage": round(pct, 1),
            "top_groups": data["groups"].most_common(5),
        }

    return risk_assessment

risk = assess_sector_risk(collector.posts)
```

### 步骤 4：追踪新兴和品牌重塑的组织

```python
def track_new_groups(posts, lookback_days=90):
    """识别新出现的勒索软件组织。"""
    group_first_seen = {}
    for post in posts:
        group = post.get("group_name", "")
        try:
            date = datetime.fromisoformat(
                post.get("discovered", "").replace("Z", "+00:00")
            )
            if group not in group_first_seen or date < group_first_seen[group]["first_seen"]:
                group_first_seen[group] = {
                    "first_seen": date,
                    "first_victim": post.get("post_title", ""),
                }
        except (ValueError, TypeError):
            continue

    cutoff = datetime.now() - timedelta(days=lookback_days)
    new_groups = {
        group: info for group, info in group_first_seen.items()
        if info["first_seen"].replace(tzinfo=None) >= cutoff
    }

    # 统计每个新组织的受害者总数
    for group in new_groups:
        victims = [p for p in posts if p.get("group_name") == group]
        new_groups[group]["total_victims"] = len(victims)
        new_groups[group]["avg_per_month"] = round(
            len(victims) / max(1, lookback_days / 30), 1
        )

    print(f"\n=== 新组织（最近 {lookback_days} 天）===")
    for group, info in sorted(new_groups.items(), key=lambda x: -x[1]["total_victims"]):
        print(f"  {group}: {info['total_victims']} 名受害者, "
              f"首次发现 {info['first_seen'].strftime('%Y-%m-%d')}")

    return new_groups

new_groups = track_new_groups(collector.posts, lookback_days=90)
```

### 步骤 5：生成情报报告

```python
def generate_ransomware_intel_report(trends, risk, new_groups):
    """生成勒索软件威胁情报报告。"""
    report = f"""# 勒索软件威胁情报报告
生成时间: {datetime.now().isoformat()}

## 执行摘要
- **追踪受害者总数**: {trends['total_posts']}
- **活跃勒索软件组织**: {trends['unique_groups']}
- **新兴组织（最近 90 天）**: {len(new_groups)}

## 最活跃组织
| 排名 | 组织 | 受害者数 |
|------|-------|---------|
"""
    for i, (group, count) in enumerate(trends["top_groups"][:10], 1):
        report += f"| {i} | {group} | {count} |\n"

    report += "\n## 新兴组织\n"
    for group, info in sorted(new_groups.items(), key=lambda x: -x[1]["total_victims"])[:10]:
        report += f"- **{group}**: {info['total_victims']} 名受害者，首次出现于 {info['first_seen'].strftime('%Y-%m-%d')}\n"

    report += "\n## 行业风险评估\n"
    report += "| 行业 | 受害者数 | 占比 | 风险级别 |\n|--------|---------|---|------------|\n"
    for sector, data in list(risk["sectors"].items())[:10]:
        report += f"| {sector} | {data['victim_count']} | {data['percentage']}% | {data['risk_level'].upper()} |\n"

    report += """
## 建议措施
1. 每日监控 DLS 情报，关注您的组织及供应链合作伙伴
2. 优先修补被最活跃组织利用的漏洞
3. 实施离线备份策略以降低勒索杠杆
4. 针对勒索软件场景开展桌面演练
5. 与行业 ISAC 和威胁共享社区共享指标
"""
    with open("ransomware_intel_report.md", "w") as f:
        f.write(report)
    print("[+] 报告已保存: ransomware_intel_report.md")
    return report

generate_ransomware_intel_report(trends, risk, new_groups)
```

## 验收标准

- 从公开追踪情报成功导入勒索软件受害者数据
- 分析组织活动趋势（含月度分类）
- 生成行业和地理风险评估
- 识别新兴组织并提供活动指标
- 生成包含可执行建议的情报报告
- 所有收集工作通过经授权的公开来源进行

## 参考资料

- [Ransomwatch GitHub](https://github.com/joshhighet/ransomwatch)
- [SOCRadar: Top Ransomware Statistics 2025](https://socradar.io/blog/top-20-ransomware-statistics-to-know-2025/)
- [Bitsight: Ransomware & Deep Web Trends](https://www.bitsight.com/underground/ransomware)
- [Sophos: Threat Intelligence Report 2025](https://www.sophos.com/en-us/blog/threat-intelligence-executive-report-volume-2025-number-6)
- [H-ISAC: Ransomware Data Leak Sites Report](https://www.aha.org/h-isac-green-reports/2025-08-26-h-isac-tlp-ransomware-data-leak-sites-report-august-26-2025)
- [CYFIRMA: Weekly Intelligence Reports](https://www.cyfirma.com/news/weekly-intelligence-report-16-january-2026/)