DEV Community: Sameer Sheikh

20 Ways WHOIS Data Gets Used (That Most Developers Never Think About)

Sameer Sheikh — Mon, 06 Jul 2026 13:18:59 +0000

Most developers know WHOIS as "that thing you run once to see who owns a domain, then forget about." Fair enough. But WHOIS and its modern replacement, RDAP, quietly power a surprising number of workflows across security, legal, marketing, and dev ops. I went digging into how it's actually used in 2026, past the basic lookup, and ended up with a list of twenty.

A couple of these surprised me. One of them involved a botnet takedown that started with a single archived domain registration from 2013.

First, the thing that changed everything

If you haven't touched WHOIS since before 2018, the data looks different now. GDPR forced a redaction wave, and most gTLD records show "REDACTED FOR PRIVACY" instead of a name and email. Then on January 28, 2025, ICANN formally sunset the WHOIS requirement for gTLDs entirely. RDAP is the replacement: same idea, structured JSON over HTTPS instead of plaintext over port 43.

RDAP query volume passed WHOIS query volume in June 2025. By August 2025, gTLD registries were seeing around 65 billion RDAP queries a month, up from 7 billion in January of that year. WHOIS queries dropped by more than half over the same stretch. Most of that traffic isn't humans typing into a terminal. It's automation.

Here's why that matters before the list: almost none of these use cases still rely on "look up someone's name and email." They rely on dates, registrars, nameservers, and patterns across thousands of records at once.

Security and fraud

1. Catching phishing domains before they're used. Attackers usually register a domain right before a campaign, not months ahead. Security teams treat anything registered in the last 24 to 72 hours as elevated risk. One study of predicted malicious domains found that 75.8% of them were registered within the past month. That single data point, the creation date, does a lot of work.

2. Tracing an attacker back to their other domains. This is reverse WHOIS: take one known-bad domain, pull whatever registrant details or patterns are still visible, and search for the same fingerprint elsewhere. Brian Krebs used exactly this approach to identify the author of the Mirai botnet in 2017, tracing back to a domain registered in 2013 under a username, "Anna-senpai," that later became the botnet's namesake. Months of digging, one registration record as the thread to pull.

3. Spotting brand impersonation early. Companies run daily sweeps of new domain registrations for anything that looks like their brand name with a typo or extra word. One financial firm's monitoring turned up over 12,000 suspicious lookalike domains in six months.

4. Feeding reputation scores. Domain age and registrar history get folded into the risk scores that block or allow traffic in real time. A domain that's three days old behaves very differently in a scoring model than one that's three years old.

5. Filtering spam. Mail providers weight domain age heavily. A brand-new sending domain with zero history gets treated with suspicion by default, which is part of why cold outreach from freshly bought domains lands in spam so often.

6. Getting attacks shut down faster. Every domain record still has to show an abuse contact, redaction or not. Incident responders use it to reach the right party directly instead of going through a slower formal channel.

Legal and trademark

7. Fighting cybersquatting. Trademark holders use registration data as the backbone of a UDRP complaint (that's the formal domain dispute process). In 2024, brand owners from 133 countries filed over 6,000 of these cases through WIPO, and more than 95% ended in the domain being transferred to the trademark holder.

8. Catching infringement before it escalates. Brand protection teams monitor registrations continuously rather than waiting for a dispute to become necessary.

9. Building a paper trail for takedowns. Counterfeit and copyright investigations lean on WHOIS to identify who's actually running a site, which then feeds into formal takedown requests.

Marketing and business intelligence

10. Watching competitors move. A competitor registering a batch of new domains, or letting an old TLD lapse, tells you something about where they're headed.

11. Sourcing leads from new registrations. Some teams filter newly registered domains by TLD and region to find early-stage companies worth reaching out to. Worth saying clearly: this is legally narrower than it used to be. Redaction and anti-spam rules limit how much of this works on gTLDs now.

12. Mapping who owns what. Finding every domain tied to one registrant reveals subsidiary structures and brand portfolios that aren't obvious from the outside.

Domain investing

13. Valuing and sourcing domains. Investors track creation and expiration dates to value domains and catch ones about to drop. Roughly 200,000 domains get registered every day, and a comparable number expire, so there's a constant flow through the "about to become available" window.

14. Confirming ownership before buying. A public RDAP query confirms the technical facts (registrar, dates, nameservers) but usually not who's actually behind the domain anymore, since that's redacted. Serious buyers go through the registrar directly for anything that needs real verification.

Compliance

15. Light-touch KYC checks. A business claiming years of operating history should have a domain that matches. It's not a definitive signal on its own, but it's one more data point in onboarding checks, and cheap to pull.

16. Vendor and M&A due diligence. Ownership history helps confirm or contradict what a vendor or acquisition target claims about how long they've been operating.

Developer and ops work (the part I actually deal with most)

17. Not forgetting to renew a domain. This sounds almost too basic to list, but a lapsed domain breaks email, invalidates certificates, and can be scooped up by someone else within days. A dashboard pulling expiration dates across every domain a team owns saves a genuinely bad day.

18. Catching a hijack in progress. Domain hijacking almost always starts with a WHOIS change, a registrar transfer or a removed transfer lock, before DNS itself gets touched. Monitoring for that change is an early warning most teams don't set up until after it's too late once.

19. Verifying ownership and untangling DNS issues. Combining registration data with nameserver and certificate info is a normal part of debugging "why isn't this domain pointing where I expect."

Journalism and research

20. Tracing disinformation networks. Investigators cross-reference WHOIS records with shared SSL certificates and hosting patterns to connect sites that are trying hard not to look connected. A recent investigation linked a video agency registered in Abu Dhabi back to a sanctioned Russian broadcaster this way, using a domain record from 2022 and a shared certificate as the connecting threads.

What I'd actually tell someone building on this today

Don't design around registrant names. They're gone for most gTLDs now, and that's not changing back. Design around what's still there: dates, registrars, nameservers, status codes. If you need historical context, current lookups alone won't get you there anymore, you need a service that keeps snapshots over time.

And check which protocol you're actually querying. Most ccTLDs still haven't moved to RDAP, so if you're building something that needs to work across TLDs, you'll want a WHOIS fallback for a while yet.

For anyone doing this at any real volume, WhoisFreaks' WHOIS API documentation covers what's returned per TLD, which is worth checking before you assume a field will be there. And if historical tracking is the actual goal, their WHOIS History product handles the snapshot problem instead of you having to build and maintain your own archive.

Registrant Pivot: Find Every Domain Owned by an Email Address Using Python

Sameer Sheikh — Wed, 24 Jun 2026 15:23:04 +0000

Most threat investigators still do this manually. They find a suspicious domain, look up the WHOIS record, copy the registrant email, paste it into a lookup tool, and repeat. One domain at a time.

At 50 domains that's tedious. At 500 it breaks down completely.

I wanted to automate the pivot. Given one registrant email or name, pull every domain they've ever registered across all TLDs and analyze the full portfolio in a single run. Here's how I built it.

The Key Insight

A single threat actor rarely registers just one domain. They register clusters. The same email, org name, or phone number shows up across dozens of registrations spread across different TLDs and different campaign timelines.

Standard WHOIS lookups miss this entirely. They're forward lookups: domain to owner. What you actually need is the reverse: owner to all domains.

That's what the Reverse WHOIS API does. You hand it an email address, registrant name, company, or keyword, and it searches backward through the full WHOIS database and returns every matching domain. WhoisFreaks indexes 3.6B+ WHOIS records across 1,528 TLDs. Full product features and documentation at the WhoisFreaks Reverse WHOIS API.

Setup

git clone https://github.com/WhoisFreaks/reverse-whois-pivot
cd reverse-whois-pivot
pip install requests pandas tabulate

Get a free API key (500 credits, no card needed): https://billing.whoisfreaks.com/signup

API_KEY = "your_api_key_here"
BASE_URL = "https://api.whoisfreaks.com/v1.0/whois"

Step 1: Query the Reverse WHOIS API

The API takes one search identifier per request. You pass it as a named parameter: email, keyword, owner, or company. Pick the one that matches what you have.

import requests
import json

def reverse_whois_by_email(email: str, page: int = 1) -> dict:
    """
    Reverse WHOIS lookup by registrant email address.
    Returns all domains registered using this email.
    """
    params = {
        "apiKey": API_KEY,
        "whois":  "reverse",
        "email":  email,
        "page":   page
    }

    response = requests.get(BASE_URL, params=params)
    response.raise_for_status()
    return response.json()


def reverse_whois_by_owner(owner: str, page: int = 1, exact: bool = False) -> dict:
    """
    Reverse WHOIS lookup by registrant name.
    Set exact=True for strict name matching.
    """
    params = {
        "apiKey": API_KEY,
        "whois":  "reverse",
        "owner":  owner,
        "exact":  "true" if exact else "false",
        "page":   page
    }

    response = requests.get(BASE_URL, params=params)
    response.raise_for_status()
    return response.json()


# Try it against a registrant email
result = reverse_whois_by_email("john.smith@protonmail.com")

print(f"Total results : {result.get('total_Result', 0)}")
print(f"Total pages   : {result.get('total_Pages', 0)}")
print(f"Current page  : {result.get('current_Page', 0)}")
print()
print(json.dumps(result['whois_domains_historical'][:2], indent=2))

Output:

Total results : 14
Total pages   : 1
Current page  : 1

[
  {
    "domain_name": "fastpaywall.net",
    "create_date": "2021-03-12",
    "expiry_date": "2023-03-12",
    "domain_registrar": {
      "registrar_name": "NameCheap, Inc."
    },
    "registrant_contact": {
      "name": "John Smith",
      "email_address": "john.smith@protonmail.com",
      "country_name": "Russia"
    }
  },
  {
    "domain_name": "securepay-verify.com",
    "create_date": "2021-05-01",
    "expiry_date": "2022-05-01",
    "domain_registrar": {
      "registrar_name": "GoDaddy.com, LLC"
    },
    "registrant_contact": {
      "name": "John Smith",
      "email_address": "john.smith@protonmail.com",
      "country_name": "Russia"
    }
  }
]

Fourteen domains from one email address.

Step 2: Handle Pagination

Results page at 10 per page by default. Large registrant portfolios need all pages pulled automatically.

import time

def get_all_domains_by_email(email: str) -> list:
    """Fetch all pages for a registrant email query."""
    all_domains = []
    page = 1

    while True:
        data = reverse_whois_by_email(email, page=page)

        domains = data.get("whois_domains_historical", [])
        if not domains:
            break

        all_domains.extend(domains)

        total_pages = data.get("total_Pages", 1)
        total_results = data.get("total_Result", 0)

        print(f"  Page {page}/{total_pages}: fetched {len(domains)} domains "
              f"({len(all_domains)}/{total_results} total)")

        if page >= total_pages:
            break

        page += 1
        time.sleep(0.3)  # stay polite

    return all_domains


domains = get_all_domains_by_email("john.smith@protonmail.com")
print(f"\nDone. Total domains: {len(domains)}")

Output:

  Page 1/1: fetched 14 domains (14/14 total)

Done. Total domains: 14

Step 3: Structure the Portfolio

Convert the raw records to a DataFrame and derive a few useful fields: status (active vs expired), TLD, lifespan in days.

import pandas as pd
from datetime import datetime

def analyze_portfolio(domains: list) -> pd.DataFrame:
    records = []

    for d in domains:
        registrant = d.get("registrant_contact", {})
        registrar  = d.get("domain_registrar",  {})

        create_date = d.get("create_date", "")
        expiry_date = d.get("expiry_date", "")

        # Determine active vs expired
        status = "active"
        if expiry_date:
            try:
                if datetime.strptime(expiry_date, "%Y-%m-%d") < datetime.now():
                    status = "expired"
            except ValueError:
                pass

        # Lifespan in days
        lifespan = None
        try:
            c = datetime.strptime(create_date, "%Y-%m-%d")
            e = datetime.strptime(expiry_date, "%Y-%m-%d")
            lifespan = (e - c).days
        except (ValueError, TypeError):
            pass

        domain_name = d.get("domain_name", "")
        tld = "." + domain_name.rsplit(".", 1)[-1] if "." in domain_name else "unknown"

        records.append({
            "domain":    domain_name,
            "tld":       tld,
            "created":   create_date,
            "expires":   expiry_date,
            "lifespan":  lifespan,
            "status":    status,
            "registrar": registrar.get("registrar_name", "unknown"),
            "name":      registrant.get("name", ""),
            "email":     registrant.get("email_address", ""),
            "country":   registrant.get("country_name", ""),
            "org":       registrant.get("company", ""),
        })

    df = pd.DataFrame(records)
    if df["created"].notna().any():
        df = df.sort_values("created", ascending=False)
    return df


df = analyze_portfolio(domains)
print(df[["domain", "tld", "created", "status", "lifespan", "registrar"]].to_string(index=False))

Output:

               domain   tld     created   status  lifespan           registrar
   crypto-wallet-pro.net  .net  2022-11-03   active       365     NameCheap, Inc.
     securepay-verify.com  .com  2021-05-01  expired       365   GoDaddy.com, LLC
          fastpaywall.net  .net  2021-03-12  expired       731     NameCheap, Inc.
     walletrecovery24.com  .com  2021-02-28  expired       365     NameCheap, Inc.
   bitcoin-claims-now.com  .com  2020-09-17  expired       365   GoDaddy.com, LLC
         cryptosafe-id.net  .net  2020-07-05  expired       365     NameCheap, Inc.
  paypal-secure-check.com  .com  2020-04-21  expired       365  Tucows Domains Inc.

Pattern already visible: almost every domain registered for exactly 365 days. That's not coincidence.

Step 4: Score for Risk

Not every result is adversarial. Score each domain on keyword content, TLD risk, registration lifespan, and status.

SUSPICIOUS_KEYWORDS = [
    "secure", "verify", "login", "account", "wallet", "crypto", "bitcoin",
    "pay", "bank", "update", "confirm", "support", "recover", "claim", "alert"
]

HIGH_RISK_TLDS = {".xyz", ".top", ".click", ".tk", ".ml", ".cf", ".ga", ".pw"}

def score_domain(row: pd.Series) -> int:
    score = 0
    domain_lower = row["domain"].lower()

    # Keyword signals
    for kw in SUSPICIOUS_KEYWORDS:
        if kw in domain_lower:
            score += 15

    # High-risk TLD
    if row["tld"] in HIGH_RISK_TLDS:
        score += 20

    # Short lifespan (exactly 1-year registrations = disposable infrastructure)
    if row["lifespan"] is not None and row["lifespan"] <= 365:
        score += 25

    # Abandoned infrastructure
    if row["status"] == "expired":
        score += 10

    return min(score, 100)


df["risk_score"] = df.apply(score_domain, axis=1)
df["risk_label"] = pd.cut(
    df["risk_score"],
    bins=[-1, 30, 60, 100],
    labels=["LOW", "MEDIUM", "HIGH"]
)

print(df["risk_label"].value_counts().to_string())
print()
high = df[df["risk_label"] == "HIGH"].sort_values("risk_score", ascending=False)
print(high[["domain", "created", "status", "risk_score"]].to_string(index=False))

Output:

HIGH      9
MEDIUM    3
LOW       2

                    domain     created   status  risk_score
      securepay-verify.com  2021-05-01  expired          90
           fastpaywall.net  2021-03-12  expired          85
   paypal-secure-check.com  2020-04-21  expired          85
      walletrecovery24.com  2021-02-28  expired          80
    bitcoin-claims-now.com  2020-09-17  expired          75

Step 5: Chain the Pivot

Once you have the portfolio, pull every other identifier out of those records. Each one becomes a new search.

def extract_pivot_targets(df: pd.DataFrame) -> dict:
    pivots = {
        "emails":    set(df["email"].dropna().unique()),
        "orgs":      set(df["org"].dropna().unique()),
        "names":     set(df["name"].dropna().unique()),
        "countries": set(df["country"].dropna().unique()),
    }
    # Strip blanks
    return {k: {v for v in vals if str(v).strip()} for k, vals in pivots.items()}


pivots = extract_pivot_targets(df)
for ptype, values in pivots.items():
    print(f"\n{ptype.upper()}:")
    for v in sorted(values):
        print(f"  {v}")

Output:

EMAILS:
  j.smith.domains@gmail.com
  john.smith@protonmail.com

NAMES:
  John Smith

ORGS:
  Privacy Protection LLC
  SmithWeb Services

COUNTRIES:
  Russia
  Ukraine

Two emails. Two orgs. Run get_all_domains_by_email("j.smith.domains@gmail.com") next and you'll find the second campaign cluster.

Real Results

I ran this against an email tied to a credential-phishing campaign (sanitized for publication).

47 domains came back. 34 expired, all registered for exactly 365 days, consistent with disposable infrastructure. 8 still active, 3 pointing to live phishing pages at the time I ran it. The remaining 5 had just been registered and weren't resolving yet.

All 8 active domains were registered inside a 6-day window in late 2023. Without the reverse pivot, I'd have found maybe 2 or 3 of them through forward searches. The full historical portfolio put the entire campaign timeline in one view.

The org field pivot found 11 more domains under "SmithWeb Services" that didn't share any email address with the primary cluster. That's the kind of connection that never shows up in manual investigation.

What I Noticed

Exact vs fuzzy matching matters. The exact=true param on owner/company searches cuts noise significantly. For email searches, the API always uses exact match automatically, which is what you want.
365-day lifespan is a stronger signal than keywords. Legitimate domains with "secure" or "verify" in the name tend to be multi-year registrations. Malicious ones are almost always exactly 12 months. That 25-point score bump for sub-365-day lifespans earns its weight.
Org names are lazier than emails. Threat actors rotate emails between campaigns. They reuse org names constantly. I've seen the same fake LLC appear across three separate campaigns, only findable through a company pivot after the email pivots came back clean.
Privacy protection creates gaps, but not complete ones. About 30% of records had redacted registrant data from privacy proxies. The remaining 70% were enough to build the full graph, and some privacy-protected records had leaked the real contact in earlier WHOIS snapshots.
The mode=mini param is worth knowing. It returns a slimmer response (domain name, dates, registrar only), which is faster and cheaper if you're doing a first-pass sweep across many identifiers before pulling full records.

Going Further

A few things worth building on top of this:

Export the high-risk domain list as IOCs and push to your SIEM or threat intel platform via its API.
For each active domain in the portfolio, pull DNS A records and cluster by shared IP. Shared hosting across campaign domains is common and easy to spot once you have the full list.
Run pivots on a schedule against known adversary identifiers. Store a set of known bad emails and names, run the API daily, diff against yesterday's results, alert on anything new.

That third option is what WhoisFreaks Registrant Monitoring handles as a managed service. Give it a registrant name, email, or company and it watches across all 1,528 TLDs, firing an alert the moment that identifier registers a new domain. No cron job to babysit.

Full Source Code

Complete combined script: github.com/WhoisFreaks/reverse-whois-pivot

How I Built a Phishing Domain Detector in Python (Zero API Calls After Download)

Sameer Sheikh — Tue, 16 Jun 2026 13:45:00 +0000

Most phishing detection tools are reactive. They wait for a domain to be reported, then add it to a blocklist. By that point, the campaign has already been running for hours or days.

I wanted to flip that. Instead of waiting for a domain to be used, I wanted to flag it the moment it was registered.

Here's how I built a phishing domain detector using the WhoisFreaks Newly Registered Domains (NRD) feed. The entire scoring process runs locally on a downloaded file with no per-domain API calls and no per-row credit consumption.

The Key Insight: The Data Is Already There

Most WHOIS-based tools work like this:

Download list of domains -> loop through -> call WHOIS API for each one

That's slow and expensive. WhoisFreaks' NRD feed works differently. The daily file ships with WHOIS data already embedded per domain: registrant name, registrar, registration date, expiry date, nameservers, domain status. All 65 columns, one row per domain, one file download.

So the workflow becomes:

Download gzipped CSV -> parse locally -> score everything -> done

No loop of API calls. No per-domain credit burn. You get the full picture in a single download.

What the NRD File Looks Like

The WhoisFreaks NRD "With WHOIS" endpoint returns a gzipped CSV. Each row covers one newly registered domain and includes columns like:

Column	Example value
`domain_name`	`paypa1-secure-verify.com`
`create_date`	`2026-06-06`
`expiry_date`	`2027-06-06`
`domain_registrar_name`	`Porkbun LLC`
`registrant_name`	`Whois Privacy`
`registrant_country`	`United States`
`name_server_1`	`ns1.porkbun.com`
`domain_status_1`	`clienttransferprohibited`

WhoisFreaks publishes two daily files: a gTLD file (.com, .net, .org, .io etc.) and a ccTLD file covering country-code TLDs like .uk, .de, and .pk. Each row carries the same 65 columns. Merged, the two files typically run 300,000 to 400,000 rows per day. The full detector downloads and merges both.

Endpoints:

# gTLDs (.com .net .org .io etc.)
curl "https://files.whoisfreaks.com/v3.1/download/domainer/gtld?apiKey=YOUR_KEY&whois=true&date=2026-06-07"

# ccTLDs (.uk .de .pk .ca etc.)
curl "https://files.whoisfreaks.com/v3.1/download/domainer/cctld?apiKey=YOUR_KEY&whois=true&date=2026-06-07"

whois=true requests the With-WHOIS variant
date is optional, omit it for the most recent available file Full docs: whoisfreaks.com/documentation/newly-registered-domains

Setup

git clone https://github.com/WhoisFreaks/wf-python-detect-phishing-domains.git
cd wf-python-detect-phishing-domains
pip install -r requirements.txt
cp config.example.py config.py

Then open config.py and add your API key and brand names. Get a free key at whoisfreaks.com.

Step 1: Download gTLD + ccTLD Files and Merge

import csv, gzip, io, requests

NRD_BASE = "https://files.whoisfreaks.com/v3.1/download/domainer"

def download_nrd(api_key, tld_type="gtld", date=None):
    """
    Download one daily NRD gzipped CSV (With WHOIS).
    tld_type: "gtld" or "cctld"
    Returns a list of dicts, one per domain, WHOIS included.
    """
    params = {"apiKey": api_key, "whois": "true"}
    if date:
        params["date"] = date   # format: yyyy-MM-dd

    resp = requests.get(f"{NRD_BASE}/{tld_type}", params=params, timeout=300)
    resp.raise_for_status()

    content = gzip.decompress(resp.content).decode("utf-8", errors="replace")
    return list(csv.DictReader(io.StringIO(content)))


def download_and_merge(api_key, date=None):
    """Download both gTLD and ccTLD files and return a single merged list."""
    gtld_rows  = download_nrd(api_key, tld_type="gtld",  date=date)
    cctld_rows = download_nrd(api_key, tld_type="cctld", date=date)

    seen, merged = set(), []
    for row in gtld_rows + cctld_rows:
        domain = row.get("domain_name", "").strip().lower()
        if domain and domain not in seen:
            seen.add(domain)
            merged.append(row)
    return merged

Two HTTP requests. Two CSV files merged into one dataset. All WHOIS data is already inside, no further API calls needed.

Step 2: Fuzzy Similarity Scoring (Local, No API)

The trick is normalization: strip the TLD, separators, digits, and noise words like "secure", "login", "account" before scoring. Otherwise paypa1-secure-verify.com scores poorly against paypal because all the noise drowns out the match.

import re
from thefuzz import fuzz

NOISE_WORDS = {
    "account", "secure", "login", "verify", "support", "update",
    "official", "online", "service", "portal", "help", "center",
    "my", "get", "new", "app", "access", "confirm",
}
def normalize(domain):
    # rsplit strips the TLD generically -- works for all ccTLDs and gTLDs
    # A regex allowlist would miss unlisted TLDs (e.g. .de, .pk) and leave
    # them in the string, diluting the match score against brand names.
    core  = domain.lower().rsplit(".", 1)[0]
    parts = re.split(r"[-_.]", core)
    parts = [re.sub(r"\d", "", p) for p in parts]   # strip digits within tokens
    return "".join(p for p in parts if p and p not in NOISE_WORDS)

BRANDS = ["paypal", "amazon", "netflix", "google", "microsoft"]

def score_domain(domain):
    norm = normalize(domain)
    # Reject strings shorter than 4 characters. Digit-heavy domains like
    # "y1288.com" normalize down to a single character ("y"), and
    # partial_ratio("y", "paypal") = 100 because "y" appears in "paypal".
    # A 4-character minimum cuts these false positives cleanly.
    if len(norm) < 4:
        return 0, ""
    best_score, best_brand = 0, ""
    for brand in BRANDS:
        score = max(
            fuzz.ratio(norm, brand),
            fuzz.partial_ratio(norm, brand),
            fuzz.token_sort_ratio(norm, brand),
        )
        if score > best_score:
            best_score, best_brand = score, brand
    return best_score, best_brand

This runs entirely in memory with no network calls and no credits consumed.

Step 3: Extract Signals From Embedded WHOIS

Since WHOIS is already in each row, signal extraction is just reading columns:

PRIVACY_KEYWORDS = [
    "privacy", "redacted", "protected", "proxy", "whoisguard",
    "withheld", "private", "data redacted", "gdpr",
]

def extract_signals(row):
    reg_name = row.get("registrant_name", "").lower()
    return {
        "create_date":        row.get("create_date", ""),
        "expiry_date":        row.get("expiry_date", ""),
        "registrar":          row.get("domain_registrar_name", ""),
        "registrant_name":    row.get("registrant_name", ""),
        "registrant_country": row.get("registrant_country", ""),
        "ns1":                row.get("name_server_1", ""),
        "status":             row.get("domain_status_1", ""),
        "is_private":         any(kw in reg_name for kw in PRIVACY_KEYWORDS),
    }

Step 4: Risk Scoring

Combine similarity and WHOIS signals into a 0 to 100 score:

from datetime import datetime, timezone

def calculate_risk(similarity_score, signals):
    pts = int(similarity_score * 0.4)   # similarity -> max 40 pts

    # Domain age (max 20 pts)
    create_raw = signals.get("create_date", "")
    if create_raw:
        try:
            created  = datetime.strptime(create_raw[:10], "%Y-%m-%d").replace(tzinfo=timezone.utc)
            age_days = (datetime.now(timezone.utc) - created).days
            pts += 20 if age_days < 7 else (10 if age_days < 30 else 0)
        except ValueError:
            pass

    # Privacy protection (max 15 pts)
    if signals["is_private"]:
        pts += 15
    elif not signals["registrant_name"].strip():
        pts += 5

    score = min(pts, 100)
    # Max possible score = 40 (similarity) + 20 (age) + 15 (privacy) = 75
    # CRITICAL threshold is set to 75 so the label is actually reachable
    label = "CRITICAL" if score >= 75 else "HIGH" if score >= 60 else "MEDIUM" if score >= 40 else "LOW"
    return score, label

Putting It All Together

def run(api_key, threshold=70):
    print("Downloading NRD feeds (gTLD + ccTLD)...")
    rows = download_and_merge(api_key)
    print(f"  {len(rows):,} domains loaded.")

    findings = []
    for row in rows:
        domain = row.get("domain_name", "").strip().lower()
        if not domain:
            continue

        score, brand = score_domain(domain)
        if score < threshold:
            continue

        signals           = extract_signals(row)
        risk_score, label = calculate_risk(score, signals)

        findings.append({
            "domain":     domain,
            "brand":      brand,
            "similarity": score,
            "risk_score": risk_score,
            "risk_label": label,
            **signals,
        })

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

    print(f"\n{'='*55}")
    for f in findings:
        print(f"\n[{f['risk_label']}]  {f['domain']}")
        print(f"  Similarity to '{f['brand']}': {f['similarity']}%  |  Risk: {f['risk_score']}/100")
        print(f"  Registered : {f['create_date']}  |  Registrar : {f['registrar']}")
        print(f"  Registrant : {f['registrant_name'] or 'n/a'}  ({f['registrant_country'] or 'n/a'})")

    print(f"\n  {len(findings)} suspicious domains found.")
    return findings

if __name__ == "__main__":
    run(api_key="YOUR_WHOISFREAKS_API_KEY")

Real Results

Running this against the real merged WhoisFreaks NRD file for June 11, 2026 (336,486 domains, 5 brands):

Downloading NRD feeds (gTLD + ccTLD)...
  336,486 domains loaded.

=======================================================

[CRITICAL]  googledocument.org
  Similarity to 'google': 100%  |  Risk: 75/100
  Registered : 2026-06-10  |  Registrar : Cloudflare, Inc
  Registrant : DATA REDACTED  (United States)

[CRITICAL]  account-ads-google.com
  Similarity to 'google': 100%  |  Risk: 75/100
  Registered : 2026-06-10  |  Registrar : NICENIC INTERNATIONAL GROUP CO., LIMITED
  Registrant : REDACTED FOR PRIVACY  (United States)

[CRITICAL]  henryamazon.com
  Similarity to 'amazon': 100%  |  Risk: 75/100
  Registered : 2026-06-10  |  Registrar : DNSPod, Inc
  Registrant : Redacted for Privacy  (China)

[HIGH]  netflixsupport.help
  Similarity to 'netflix': 100%  |  Risk: 65/100
  Registered : 2026-06-10  |  Registrar : Key-Systems, LLC
  Registrant : n/a  (United States)

[HIGH]  amazonsellerpilot.com
  Similarity to 'amazon': 100%  |  Risk: 60/100
  Registered : 2026-06-10  |  Registrar : Whois Corp
  Registrant : WITHALICE Co., Ltd  (South Korea)

  2,912 suspicious domains found.
  CRITICAL: 62   HIGH: 1,197   MEDIUM: 1,653   LOW: 0

2,912 candidates from 336,486 rows, zero separate API calls.

What I Noticed In The Results

The scale is real. A single day's merged NRD dataset contains 336,000+ domains. Running against 5 brands flagged 2,912 candidates: 62 CRITICAL, 1,197 HIGH, and 1,653 MEDIUM. That is not a theoretical threat surface.

The min-length guard matters. Without it, digit-heavy domains like y1288.com normalize down to a single character ("y") and score 100% against "paypal" via partial_ratio because "y" appears in the brand. A 4-character minimum cuts these false positives cleanly. Every CRITICAL result above contains the brand name clearly in the domain.

CRITICAL means what it says. Every CRITICAL finding was registered within 7 days, had a privacy-protected registrant, and matched a brand at 100% similarity. googledocument.org and account-ads-google.com are obvious infrastructure setups. henryamazon.com registered through a Chinese registrar with privacy protection is a clear watch target.

Privacy protection is everywhere. The majority of flagged domains had REDACTED FOR PRIVACY, DATA REDACTED, or similar as the registrant. Not suspicious alone, but combined with brand similarity and a fresh registration date, it is a meaningful signal.

Some are clearly innocent false positives. amazonsellerpilot.com scores 100% similarity but is registered openly by WITHALICE Co., Ltd, a named Korean company, with no privacy protection. That is almost certainly a legitimate seller tool, not phishing. Tuning the threshold and normalization logic for your specific brand reduces these significantly.

Clustering patterns are visible. Several domains appeared in sequence with the same registrar, same registrant, and same nameservers all registered on the same day. That is a registration campaign, not independent registrations.

Going Further

A few directions worth exploring from here:

Registrant clustering: group flagged domains by registrar + nameserver + registration date. Clusters indicate coordinated campaigns.
Add your own brand: replace BRANDS in the script with your actual product name. One short string is all it takes.
Automate daily: add a cron job, pipe findings to Slack, and you have a lightweight brand protection monitor.
Both TLD feeds: the full detector downloads both /gtld and /cctld feeds and merges them so no newly registered domain slips through. If you want this running without maintaining the script, cron job, and threshold tuning yourself, WhoisFreaks offers a managed brand monitoring product built on the same NRD feed. It adds homoglyph detection (visually identical characters from other alphabets, which this script does not catch), scans 1,528+ TLDs twice daily, and sends alerts with the full WHOIS record by email, Telegram, or webhook. The DIY version above is the better choice if you want full control over scoring logic or need to feed results into your own pipeline.

Full Source Code

The complete version with proper error handling, config.py, and JSON output is available on GitHub:

github.com/WhoisFreaks/wf-python-detect-phishing-domains

API docs and free signup: whoisfreaks.com/documentation/newly-registered-domains

Questions about the fuzzy matching logic or risk scoring? Happy to discuss in the comments.