Developer Reference

Domain Similarity

Explore Domain Similarity API documentation, request parameters, response fields, code examples, and error handling for DomScan integrations.

Domain Similarity

Compare two domains for visual and textual similarity using multiple algorithms including Levenshtein distance, Jaro-Winkler similarity, and visual homoglyph analysis. Essential for detecting typosquatting, brand impersonation, and phishing domains.

GET /v1/similarity

Query Parameters

Parameter	Type	Description
domain1 required	string	First domain (typically the legitimate domain)
domain2 required	string	Second domain (potentially suspicious domain)

Similarity Algorithms

Algorithm	Description
`levenshtein`	Edit distance normalized (0-1)
`jaro_winkler`	Prefix-weighted string similarity
`visual`	Homoglyph/lookalike character detection

Example Request

curl -H "X-API-Key: your-api-key" "https://domscan.net/v1/similarity?domain1=paypal.com&domain2=paypa1.com"

import requests

domscan = requests.Session()
domscan.headers.update({"X-API-Key": "your-api-key"})

# Check multiple suspicious domains
legit = "paypal.com"
suspects = ["paypa1.com", "paypaI.com", "pаypal.com"]  # Note: last one has Cyrillic 'а'

for suspect in suspects:
    response = domscan.get(
        "https://domscan.net/v1/similarity",
        params={"domain1": legit, "domain2": suspect}
    )
    data = response.json()
    print(f"{suspect}: {data['typosquatting_risk']} risk (visual: {data['similarity']['visual']:.2f})")

Example Response

{
  "domain1": "paypal.com",
  "domain2": "paypa1.com",
  "similarity": {
    "levenshtein": 0.86,
    "jaro_winkler": 0.93,
    "visual": 0.95
  },
  "is_similar": true,
  "typosquatting_risk": "high",
  "homoglyphs_detected": ["l → 1"],
  "risk_factors": ["character_substitution", "high_visual_similarity"]
}

Response Fields

Field	Type
`domain1`	string
`domain2`	string
`similarity_score`	number
`visual_similarity`	number
`textual_similarity`	number
`is_confusable`	boolean
`risk_level`	string