Analyzing Typosquatting Domains With DnstwistSAFE

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The instruction file, verbatim from the audited commit — this is the text the model reads, and the surface the audit's instruction layer examines. Quoted here so you can judge it without cloning anything.

---
name: analyzing-typosquatting-domains-with-dnstwist
description: Generate domain permutations with dnstwist and check DNS resolution
  to detect typosquatting, homograph phishing, and brand impersonation domains registered
  against your organization. Use when asked to monitor for lookalike domains, investigate
  a phishing domain, or assess brand-impersonation risk.
domain: cybersecurity
subdomain: threat-intelligence
tags:
- dnstwist
- typosquatting
- phishing
- domain-monitoring
- brand-protection
- homograph
- dns
- threat-intelligence
version: '1.0'
author: mahipal
license: Apache-2.0
atlas_techniques:
- AML.T0073
- AML.T0052
nist_csf:
- ID.RA-01
- ID.RA-05
- DE.CM-01
- DE.AE-02
mitre_attack:
- T1583.001
- T1566.002
- T1598.003
- T1583.006
mitre_f3:
  version: '1.1'
  tactics:
  - resource-development
  - reconnaissance
  - initial-access
  techniques:
  - id: T1583.001
    name: 'Acquire Infrastructure: Domains'
    tactic: resource-development
    source: attack
  - id: F1020.002
    name: 'Create Fake Materials: Fake Website'
    tactic: resource-development
    source: f3
  - id: T1598
    name: Phishing for Information
    tactic: reconnaissance
    source: attack
  - id: T1593
    name: Search Open Websites/Domains
    tactic: reconnaissance
    source: attack
  - id: T1660
    name: Phishing
    tactic: initial-access
    source: attack
---
# 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 recor

SAFEgrade B · trust 89/100 Nothing in the source contradicts what it says it does. Grade A is reserved for packages that have also passed the behavioural sandbox.

What the source does

Filesystem

none-observed

Network

none-observed

Shell

none-observed

Dependencies

pinned

Secrets in source

none-found

Findings (0)

No findings outside the package's declared scope.

Gates applied: no_behavioural_pass.