building-adversary-infrastructure-tracking-system

构建对手基础设施跟踪系统

概述

对手基础设施跟踪利用被动 DNS 记录、证书透明度日志、WHOIS 注册数据和 IP 富化来发现、映射和监控威胁行为者的命令与控制（C2）网络。攻击者经常在不同行动中重复使用托管服务商、注册商、SSL 证书和命名模式，使分析师能够从已知指标出发，发现新的基础设施。本技能涵盖构建一个自动化跟踪系统，该系统可识别基础设施关系、检测与对手模式匹配的新注册域名，并维护持续更新的威胁行为者网络地图。

何时使用

• 在您的环境中部署或配置构建对手基础设施跟踪系统功能时
• 在建立符合合规要求的安全控制措施时
• 在为该领域构建或改进安全架构时
• 在开展需要此实施的安全评估时

前提条件

• Python 3.9+，并安装 requests、dnspython、python-whois、shodan、networkx 库
• API 密钥：SecurityTrails、PassiveTotal/RiskIQ、Shodan、VirusTotal
• 可访问被动 DNS 数据源
• 了解 DNS 基础设施、托管和域名注册
• 用于关系可视化的图数据库（Neo4j）或 NetworkX

核心概念

被动 DNS

被动 DNS 捕获历史 DNS 解析数据，记录哪些域名在何时解析到哪些 IP。与主动 DNS 查询不同，被动 DNS 即使在记录变更后仍保留历史关系，使分析师能够跟踪基础设施变化、识别共享托管模式，并发现随时间解析到相同 IP 地址的相关域名。

基础设施拓线

拓线通过追踪连接来识别相关基础设施：IP 拓线（查找某个 IP 上的所有域名）、域名拓线（查找某个域名解析到的所有 IP）、WHOIS 拓线（查找具有相同注册人的域名）、证书拓线（查找共享 SSL 证书的主机），以及 NS/MX 拓线（查找使用相同名称服务器或邮件服务器的域名）。

对手基础设施模式

威胁行为者表现出一些模式：偏好的注册商（Namecheap、REG.RU、Tucows）、偏好的托管服务（防弹托管服务商、云服务）、域名生成算法（DGA）、一致的命名模式，以及跨行动重复使用证书。

工作流

步骤 1：被动 DNS 基础设施发现

import requests
import json
from collections import defaultdict
from datetime import datetime

class InfrastructureTracker:
    def __init__(self, securitytrails_key=None, vt_key=None, shodan_key=None):
        self.st_key = securitytrails_key
        self.vt_key = vt_key
        self.shodan_key = shodan_key
        self.infrastructure_graph = defaultdict(lambda: {"nodes": set(), "edges": []})

    def passive_dns_lookup(self, domain):
        """Query passive DNS for domain resolution history."""
        headers = {"apikey": self.st_key}
        url = f"https://api.securitytrails.com/v1/history/{domain}/dns/a"
        resp = requests.get(url, headers=headers, timeout=30)
        if resp.status_code == 200:
            records = resp.json().get("records", [])
            history = []
            for record in records:
                for value in record.get("values", []):
                    history.append({
                        "domain": domain,
                        "ip": value.get("ip", ""),
                        "first_seen": record.get("first_seen", ""),
                        "last_seen": record.get("last_seen", ""),
                        "type": record.get("type", "a"),
                    })
            print(f"[+] Passive DNS for {domain}: {len(history)} records")
            return history
        return []

    def reverse_ip_lookup(self, ip_address):
        """Find all domains hosted on an IP address."""
        headers = {"apikey": self.st_key}
        url = f"https://api.securitytrails.com/v1/ips/nearby/{ip_address}"
        resp = requests.get(url, headers=headers, timeout=30)
        if resp.status_code == 200:
            blocks = resp.json().get("blocks", [])
            domains = []
            for block in blocks:
                for site in block.get("sites", []):
                    domains.append(site)
            print(f"[+] Reverse IP for {ip_address}: {len(domains)} domains")
            return domains
        return []

    def whois_lookup(self, domain):
        """Get WHOIS registration data for pivoting."""
        headers = {"apikey": self.st_key}
        url = f"https://api.securitytrails.com/v1/domain/{domain}/whois"
        resp = requests.get(url, headers=headers, timeout=30)
        if resp.status_code == 200:
            data = resp.json()
            whois_data = {
                "domain": domain,
                "registrar": data.get("registrar", ""),
                "registrant_org": data.get("registrant_org", ""),
                "registrant_email": data.get("registrant_email", ""),
                "name_servers": data.get("nameServers", []),
                "created_date": data.get("createdDate", ""),
                "updated_date": data.get("updatedDate", ""),
                "expires_date": data.get("expiresDate", ""),
            }
            return whois_data
        return {}

    def pivot_from_seed(self, seed_indicator, indicator_type="domain", depth=2):
        """Recursively pivot from a seed indicator to discover infrastructure."""
        discovered = {"domains": set(), "ips": set(), "relationships": []}

        if indicator_type == "domain":
            discovered["domains"].add(seed_indicator)
            # Get IPs for domain
            pdns = self.passive_dns_lookup(seed_indicator)
            for record in pdns:
                ip = record["ip"]
                discovered["ips"].add(ip)
                discovered["relationships"].append({
                    "source": seed_indicator, "target": ip,
                    "type": "resolves_to",
                    "first_seen": record["first_seen"],
                    "last_seen": record["last_seen"],
                })

                if depth > 1:
                    # Reverse lookup on discovered IPs
                    reverse_domains = self.reverse_ip_lookup(ip)
                    for rd in reverse_domains[:20]:
                        discovered["domains"].add(rd)
                        discovered["relationships"].append({
                            "source": rd, "target": ip,
                            "type": "hosted_on",
                        })

        elif indicator_type == "ip":
            discovered["ips"].add(seed_indicator)
            domains = self.reverse_ip_lookup(seed_indicator)
            for domain in domains[:20]:
                discovered["domains"].add(domain)
                discovered["relationships"].append({
                    "source": domain, "target": seed_indicator,
                    "type": "hosted_on",
                })

        print(f"[+] Pivot from {seed_indicator}: "
              f"{len(discovered['domains'])} domains, "
              f"{len(discovered['ips'])} IPs, "
              f"{len(discovered['relationships'])} relationships")
        return discovered

tracker = InfrastructureTracker(
    securitytrails_key="YOUR_ST_KEY",
    vt_key="YOUR_VT_KEY",
)

步骤 2：构建基础设施图

import networkx as nx

class InfrastructureGraph:
    def __init__(self):
        self.graph = nx.Graph()

    def add_discovery(self, discovery_data):
        """Add discovered infrastructure to graph."""
        for domain in discovery_data["domains"]:
            self.graph.add_node(domain, type="domain")
        for ip in discovery_data["ips"]:
            self.graph.add_node(ip, type="ip")
        for rel in discovery_data["relationships"]:
            self.graph.add_edge(
                rel["source"], rel["target"],
                relationship=rel["type"],
                first_seen=rel.get("first_seen", ""),
                last_seen=rel.get("last_seen", ""),
            )

    def find_clusters(self):
        """Identify infrastructure clusters."""
        components = list(nx.connected_components(self.graph))
        clusters = []
        for component in components:
            domains = [n for n in component if self.graph.nodes[n].get("type") == "domain"]
            ips = [n for n in component if self.graph.nodes[n].get("type") == "ip"]
            clusters.append({
                "size": len(component),
                "domains": sorted(domains),
                "ips": sorted(ips),
                "domain_count": len(domains),
                "ip_count": len(ips),
            })
        clusters.sort(key=lambda x: x["size"], reverse=True)
        print(f"[+] Infrastructure clusters: {len(clusters)}")
        return clusters

    def find_hub_nodes(self, top_n=10):
        """Find high-centrality nodes (shared infrastructure)."""
        centrality = nx.degree_centrality(self.graph)
        top_nodes = sorted(centrality.items(), key=lambda x: x[1], reverse=True)[:top_n]
        hubs = []
        for node, score in top_nodes:
            hubs.append({
                "node": node,
                "type": self.graph.nodes[node].get("type", "unknown"),
                "centrality": round(score, 4),
                "connections": self.graph.degree(node),
            })
        return hubs

    def export_graph(self, output_file="infrastructure_graph.json"):
        data = nx.node_link_data(self.graph)
        with open(output_file, "w") as f:
            json.dump(data, f, indent=2)
        print(f"[+] Graph exported: {self.graph.number_of_nodes()} nodes, "
              f"{self.graph.number_of_edges()} edges")

infra_graph = InfrastructureGraph()
discovery = tracker.pivot_from_seed("evil-domain.com", depth=2)
infra_graph.add_discovery(discovery)
clusters = infra_graph.find_clusters()
hubs = infra_graph.find_hub_nodes()
infra_graph.export_graph()

步骤 3：监控新基础设施

import time

class InfrastructureMonitor:
    def __init__(self, tracker, known_indicators):
        self.tracker = tracker
        self.known = set(known_indicators)
        self.alerts = []

    def check_new_registrations(self, patterns):
        """Check for newly registered domains matching adversary patterns."""
        import re
        new_domains = []
        for pattern in patterns:
            # Query SecurityTrails for new domains matching pattern
            headers = {"apikey": self.tracker.st_key}
            url = "https://api.securitytrails.com/v1/domains/list"
            params = {"include_ips": "true", "page": 1}
            body = {"filter": {"keyword": pattern}}
            resp = requests.post(url, headers=headers, json=body, timeout=30)
            if resp.status_code == 200:
                records = resp.json().get("records", [])
                for record in records:
                    domain = record.get("hostname", "")
                    if domain not in self.known:
                        new_domains.append({
                            "domain": domain,
                            "pattern_matched": pattern,
                            "first_seen": datetime.now().isoformat(),
                        })
                        self.known.add(domain)

        if new_domains:
            print(f"[ALERT] {len(new_domains)} new domains matching patterns")
            self.alerts.extend(new_domains)
        return new_domains

    def generate_infrastructure_report(self, clusters, hubs):
        report = f"""# Adversary Infrastructure Tracking Report
Generated: {datetime.now().isoformat()}

## Summary
- Infrastructure clusters identified: {len(clusters)}
- Total domains tracked: {sum(c['domain_count'] for c in clusters)}
- Total IPs tracked: {sum(c['ip_count'] for c in clusters)}
- New domains detected: {len(self.alerts)}

## Top Infrastructure Hubs
| Node | Type | Connections | Centrality |
|------|------|-------------|------------|
"""
        for hub in hubs[:10]:
            report += (f"| {hub['node']} | {hub['type']} "
                       f"| {hub['connections']} | {hub['centrality']} |\n")

        report += "\n## Infrastructure Clusters\n"
        for i, cluster in enumerate(clusters[:5], 1):
            report += f"\n### Cluster {i} ({cluster['size']} nodes)\n"
            report += f"- Domains: {', '.join(cluster['domains'][:5])}\n"
            report += f"- IPs: {', '.join(cluster['ips'][:5])}\n"

        with open("infrastructure_report.md", "w") as f:
            f.write(report)
        print("[+] Infrastructure report saved")

monitor = InfrastructureMonitor(tracker, known_indicators=set())

验证标准

• 被动 DNS 查询返回历史解析数据
• 反向 IP 查询发现同主机托管的域名
• 基础设施拓线从种子指标扩展
• 图分析识别集群和枢纽节点
• 通过模式监控检测到新基础设施
• 生成带有可操作建议的报告

参考资料

• Juniper：使用被动 DNS 进行威胁狩猎
• Censys：高级持续性基础设施跟踪
• Embee Research：使用 DNS 拓线分析恶意软件基础设施
• Validin：被动 DNS 威胁狩猎
• SecurityTrails API
• Hunt.io：揭示恶意基础设施