analyzing-typosquatting-domains-with-dnstwist

Files (4)

• LICENSE
• SKILL.md
• api-reference.md · references/
• agent.py · scripts/

SKILL.md (first lines)

# Analyzing Typosquatting Domains with DNSTwist

## Overview

DNSTwist is a domain name permutation engine that generates similar-looking domain names to detect typosquatting, homograph phishing attacks, and brand impersonation. It creates thousands of domain permutations using techniques like character substitution, transposition, insertion, omission, and homoglyph replacement, then checks DNS records (A, AAAA, NS, MX), calculates web page similarity using fuzzy hashing (ssdeep) and perceptual hashing (pHash), and identifies potentially malicious registered domains.


## When to Use

- When investigating security incidents that require analyzing typosquatting domains with dnstwist
- When building detection rules or threat hunting queries for this domain
- When SOC analysts need structured procedures for this analysis type
- When validating security monitoring coverage for related attack techniques

## Prerequisites

- Python 3.9+ with `dnstwist` installed (`pip install dnstwist[full]`)
- Optional: GeoIP database for IP geolocation
- Optional: Shodan API key for enrichment
- Network access to perform DNS queries
- Understanding of DNS record types and domain registration

## Key Concepts

### Domain Permutation Techniques

DNSTwist generates permutations using: addition (appending characters), bitsquatting (bit-flip errors), homoglyph (visually similar Unicode characters like rn vs m), hyphenation (adding hyphens), insertion (inserting characters), omission (removing characters), repetition (repeating characters), replacement (replacing with adjacent keyboard keys), subdomain (inserting dots), transposition (swapping adjacent characters), vowel-swap (swapping vowels), and dictionary-based (appending common words).

### Fuzzy Hashing and Visual Similarity

DNSTwist uses ssdeep (locality-sensitive hash) to compare HTML content and pHash (perceptual hash) to compare screenshots of web pages. This helps identify cloned phishing sites that visually mimic the legitimate site. A high similarity score indicates a likely phishing page.

### Detection Workflow

The typical workflow is: generate domain permutations -> resolve DNS records -> check for registered domains -> compare web page similarity -> flag suspicious domains -> alert security team -> request takedown. For a typical corporate domain, dnstwist generates 5,000-10,000 permutations.

## Workflow

### Step 1: Basic Domain Permutation Scan

```python
import subprocess
import json
import csv
from datetime import datetime

def run_dnstwist_scan(domain, output_file=None):
    """Run dnstwist scan against a target domain."""
    cmd = [
        "dnstwist",
        "--registered",     # Only show registered domains
        "--format", "json", # Output in JSON
        "--nameservers", "8.8.8.8,1.1.1.1",
        "--threads", "50",
        "--mxcheck",        # Check MX records
        "--ssdeep",         # Fuzzy hash comparison
        "--geoip",          # GeoIP lookup
        domain,
    ]

    print(f"[*] Scanning permutations for: {domain}")
    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"[+] Found {len(registered)} registered lookalike domains")

        if output_file:
            with open(output_file, "w") as f:
                json.dump(registered, f, indent=2)
            print(f"[+] Results saved to {output_file}")

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

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

### Step 2: Analyze and Prioritize Results

```python
def analyze_results(results, legitimate_ips=None):
    """Analyze dnstwist results and prioritize threats."""
    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 = []

        # High similarity to legitimate site
        if ssdeep_score and ssdeep_score > 50:
            risk_score += 40
            risk_factors.append(f"high web similarity ({ssdeep_score}%)")

        # Has MX records (can receive email / phishing)
        if dns_mx:
            risk_score += 20
            risk_factors.append("has MX records (email capable)")

        # Recently registered (if whois data available)
        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"recently registered ({age_days} days)")

From mukul975/Anthropic-Cybersecurity-Skills, Apache-2.0.

More from mukul975/Anthropic-Cybersecurity-Skills