Analyzing Typosquatting Domains With Dnstwist

使用 dnstwist 分析域名抢注

概述

dnstwist 是一款域名置换引擎，用于生成外观相似的域名，以检测域名抢注、同形字符钓鱼（Phishing）攻击和品牌冒充。它使用字符替换、字符转置、字符插入、字符删除和同形字符替换等技术生成数千个域名变体，然后检查 DNS 记录（A、AAAA、NS、MX），使用模糊哈希（ssdeep）和感知哈希（pHash）计算网页相似度，并识别潜在恶意的已注册域名。

前置条件

• Python 3.9+，安装 dnstwist（pip install dnstwist[full]）
• 可选：用于 IP 地理位置的 GeoIP 数据库
• 可选：用于富化的 Shodan API 密钥
• 执行 DNS 查询的网络访问权限
• 了解 DNS 记录类型和域名注册流程

核心概念

域名置换技术

dnstwist 使用以下方式生成变体：addition（附加字符）、bitsquatting（位翻转错误）、homoglyph（视觉相似的 Unicode 字符，如 rn vs m）、hyphenation（添加连字符）、insertion（插入字符）、omission（删除字符）、repetition（重复字符）、replacement（替换为相邻键盘按键）、subdomain（插入点）、transposition（交换相邻字符）、vowel-swap（交换元音）和基于字典的（附加常用词）。

模糊哈希和视觉相似度

dnstwist 使用 ssdeep（局部敏感哈希）比较 HTML 内容，使用 pHash（感知哈希）比较网页截图。这有助于识别视觉上模仿合法站点的克隆钓鱼站点。高相似度分数表明可能是钓鱼页面。

检测工作流程

典型工作流程为：生成域名变体 → 解析 DNS 记录 → 检查已注册域名 → 比较网页相似度 → 标记可疑域名 → 通知安全团队 → 申请域名撤销。对于典型企业域名，dnstwist 会生成 5,000-10,000 个变体。

实践步骤

步骤 1：基础域名置换扫描

import subprocess
import json
import csv
from datetime import datetime

def run_dnstwist_scan(domain, output_file=None):
    """对目标域名执行 dnstwist 扫描。"""
    cmd = [
        "dnstwist",
        "--registered",     # 只显示已注册的域名
        "--format", "json", # JSON 格式输出
        "--nameservers", "8.8.8.8,1.1.1.1",
        "--threads", "50",
        "--mxcheck",        # 检查 MX 记录
        "--ssdeep",         # 模糊哈希比对
        "--geoip",          # GeoIP 查询
        domain,
    ]

    print(f"[*] 正在扫描 {domain} 的置换变体")
    result = subprocess.run(cmd, capture_output=True, text=True, timeout=600)

    if result.returncode == 0:
        results = json.loads(result.stdout)
        registered = [r for r in results if r.get("dns_a") or r.get("dns_aaaa")]
        print(f"[+] 找到 {len(registered)} 个已注册的仿冒域名")

        if output_file:
            with open(output_file, "w") as f:
                json.dump(registered, f, indent=2)
            print(f"[+] 结果已保存到 {output_file}")

        return registered
    else:
        print(f"[-] dnstwist 错误: {result.stderr}")
        return []

results = run_dnstwist_scan("example.com", "typosquat_results.json")

步骤 2：分析并优先排序结果

def analyze_results(results, legitimate_ips=None):
    """分析 dnstwist 结果并按威胁优先排序。"""
    legitimate_ips = legitimate_ips or set()
    high_risk = []
    medium_risk = []
    low_risk = []

    for entry in results:
        domain = entry.get("domain", "")
        fuzzer = entry.get("fuzzer", "")
        dns_a = entry.get("dns_a", [])
        dns_mx = entry.get("dns_mx", [])
        ssdeep_score = entry.get("ssdeep_score", 0)

        risk_score = 0
        risk_factors = []

        # 与合法站点的高度相似性
        if ssdeep_score and ssdeep_score > 50:
            risk_score += 40
            risk_factors.append(f"高度网页相似性 ({ssdeep_score}%)")

        # 有 MX 记录（可接收邮件 / 钓鱼）
        if dns_mx:
            risk_score += 20
            risk_factors.append("有 MX 记录（具备邮件能力）")

        # 近期注册（如果有 WHOIS 数据）
        whois_created = entry.get("whois_created", "")
        if whois_created:
            try:
                created = datetime.fromisoformat(whois_created.replace("Z", "+00:00"))
                age_days = (datetime.now(created.tzinfo) - created).days
                if age_days < 30:
                    risk_score += 30
                    risk_factors.append(f"近期注册（{age_days} 天前）")
                elif age_days < 90:
                    risk_score += 15
                    risk_factors.append(f"{age_days} 天前注册")
            except (ValueError, TypeError):
                pass

        # 同形字符攻击风险最高
        if fuzzer == "homoglyph":
            risk_score += 25
            risk_factors.append("同形字符（视觉上完全相同）")
        elif fuzzer in ("addition", "replacement", "transposition"):
            risk_score += 10
            risk_factors.append(f"置换类型: {fuzzer}")

        # 未指向合法基础设施
        if dns_a and not set(dns_a).intersection(legitimate_ips):
            risk_score += 10
            risk_factors.append("IP 与合法服务器不同")

        entry["risk_score"] = risk_score
        entry["risk_factors"] = risk_factors

        if risk_score >= 50:
            high_risk.append(entry)
        elif risk_score >= 25:
            medium_risk.append(entry)
        else:
            low_risk.append(entry)

    high_risk.sort(key=lambda x: x["risk_score"], reverse=True)
    medium_risk.sort(key=lambda x: x["risk_score"], reverse=True)

    print(f"\n=== 域名抢注分析 ===")
    print(f"高风险: {len(high_risk)}")
    print(f"中风险: {len(medium_risk)}")
    print(f"低风险: {len(low_risk)}")

    if high_risk:
        print(f"\n--- 高风险域名 ---")
        for entry in high_risk[:10]:
            print(f"  {entry['domain']} (分数: {entry['risk_score']})")
            for factor in entry['risk_factors']:
                print(f"    - {factor}")

    return {"high": high_risk, "medium": medium_risk, "low": low_risk}

analysis = analyze_results(results, legitimate_ips={"93.184.216.34"})

步骤 3：持续监控流水线

import time
import hashlib

class TyposquatMonitor:
    def __init__(self, domains, known_domains_file="known_typosquats.json"):
        self.domains = domains
        self.known_file = known_domains_file
        self.known_domains = self._load_known()

    def _load_known(self):
        try:
            with open(self.known_file, "r") as f:
                return json.load(f)
        except FileNotFoundError:
            return {}

    def _save_known(self):
        with open(self.known_file, "w") as f:
            json.dump(self.known_domains, f, indent=2)

    def scan_all_domains(self):
        """扫描所有监控域名，发现新的域名抢注。"""
        new_findings = []
        for domain in self.domains:
            results = run_dnstwist_scan(domain)
            for entry in results:
                domain_key = entry.get("domain", "")
                if domain_key not in self.known_domains:
                    entry["first_seen"] = datetime.now().isoformat()
                    entry["monitored_domain"] = domain
                    self.known_domains[domain_key] = entry
                    new_findings.append(entry)
                    print(f"  [新发现] {domain_key} ({entry.get('fuzzer', '')})")

        self._save_known()
        print(f"\n[+] 新发现域名抢注: {len(new_findings)} 个")
        return new_findings

    def generate_alert(self, findings):
        """为新发现的高风险域名抢注生成告警。"""
        analysis = analyze_results(findings)
        alerts = []
        for entry in analysis["high"]:
            alerts.append({
                "severity": "HIGH",
                "domain": entry["domain"],
                "target": entry.get("monitored_domain", ""),
                "risk_score": entry["risk_score"],
                "risk_factors": entry["risk_factors"],
                "dns_a": entry.get("dns_a", []),
                "dns_mx": entry.get("dns_mx", []),
                "timestamp": datetime.now().isoformat(),
            })
        return alerts

monitor = TyposquatMonitor(["mycompany.com", "mycompany.org"])
new_findings = monitor.scan_all_domains()
alerts = monitor.generate_alert(new_findings)

步骤 4：导出封锁列表和撤销报告

def export_blocklist(analysis, output_file="blocklist.txt"):
    """将高风险域名导出为防火墙/代理的封锁列表。"""
    domains = []
    for entry in analysis["high"] + analysis["medium"]:
        domain = entry.get("domain", "")
        if domain:
            domains.append(domain)

    with open(output_file, "w") as f:
        f.write(f"# 域名抢注封锁列表，生成时间 {datetime.now().isoformat()}\n")
        for d in sorted(set(domains)):
            f.write(f"{d}\n")

    print(f"[+] 封锁列表已保存: {len(domains)} 个域名 -> {output_file}")
    return domains

def generate_takedown_report(high_risk_domains):
    """生成域名撤销申请报告。"""
    report = f"""# 域名撤销申请
生成时间: {datetime.now().isoformat()}

## 摘要
识别到 {len(high_risk_domains)} 个潜在的域名抢注/钓鱼域名。

## 需要撤销的域名
"""
    for entry in high_risk_domains:
        report += f"""
### {entry['domain']}
- **置换类型**: {entry.get('fuzzer', 'unknown')}
- **IP 地址**: {', '.join(entry.get('dns_a', ['N/A']))}
- **MX 记录**: {', '.join(entry.get('dns_mx', ['N/A']))}
- **风险分数**: {entry.get('risk_score', 0)}
- **风险因素**: {'; '.join(entry.get('risk_factors', []))}
- **网页相似度**: {entry.get('ssdeep_score', 'N/A')}%
"""
    with open("takedown_report.md", "w") as f:
        f.write(report)
    print("[+] 撤销报告已生成: takedown_report.md")

export_blocklist(analysis)
generate_takedown_report(analysis["high"])

验收标准

• dnstwist 为目标域名生成域名置换变体
• DNS 解析识别已注册的仿冒域名
• 网页相似度评分检测出克隆钓鱼页面
• 风险评分按威胁级别对域名进行优先排序
• 持续监控检测新注册的域名抢注
• 正确生成封锁列表和撤销报告

参考资料

• dnstwist GitHub Repository
• dnstwister Online Service
• HawkEye: Detect Typosquatting with DNSTwist
• Darktrace: Monitoring Typosquatting Domains
• Security Risk Advisors: Domain Monitoring
• Conscia: How to Detect Typosquatting