Fraud Prevention

Fraud Detection
MCP Services

Ten MCP services for the Fraud Prevention team — each callable by any AI assistant to detect phishing sites, brand impersonation, fake e-commerce, merchant fraud, typosquatting, payment page threats, fake reviews, credential harvesting, social engineering, and fraud network mapping using AI vision, content analysis, and the 100M+ domain database.

1Phishing Site Detector

Analyzes URLs and domains for phishing indicators using AI content analysis, visual similarity detection, and domain database cross-referencing. Checks SSL certificates, domain age, content patterns, and known phishing signatures to generate a comprehensive threat assessment.

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MCP Tool Definition
Web Scraping GPT-4o Vision Domain DB SSL Check
phishing_site_detector url: string — Target URL or domain to analyze (e.g. "secure-banklogin.xyz") reference_brand: string — Legitimate brand domain to compare against (e.g. "chase.com") deep_scan: boolean — Include visual similarity, form analysis, and redirect chain (default: true)
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AI Processing Pipeline
PROCESSING PIPELINE ════════════════════════════════════════════════════════════ Step 1: Query domain DB for domain age, PageRank, IAB category, country Step 2: Check SSL certificate issuer, validity, and subject mismatch Step 3: Scrape page content and extract forms, scripts, and external resources Step 4: Screenshot page and run GPT-4o Vision to compare against reference brand Step 5: Analyze form actions, hidden fields, and data exfiltration endpoints Step 6: Check URL structure for obfuscation patterns (homoglyphs, excessive subdomains) Step 7: Generate phishing confidence score with detailed indicator breakdown
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Example Output
MCP RESPONSE — phishing_site_detector ════════════════════════════════════════════════════════════ secure-banklogin.xyz | Reference: chase.com PHISHING CONFIDENCE: 96/100 (CONFIRMED PHISHING) DOMAIN SIGNALS: Domain age: 3 days — registered 2026-02-27 PageRank: 0.0/10 — no web authority IAB Category: Uncategorized (not in 100M DB) Registrar: Budget registrar, privacy-shielded WHOIS SSL ANALYSIS: Certificate: Let's Encrypt DV — free automated certificate Subject: *.secure-banklogin.xyz — wildcard on suspicious domain Valid: 90-day cert (legitimate banks use EV certificates) VISUAL SIMILARITY: Logo match: 98.7% match to Chase logo (stolen asset) Layout match: 94.2% similar to chase.com/login Color scheme: Identical blue #0060A9 palette FORM ANALYSIS: Login form action: POST to external IP 185.234.xx.xx (offshore hosting) Hidden fields: 3 hidden inputs capturing device fingerprint Password field: Autocomplete=off (prevents browser password manager) VERDICT: PHISHING — Chase brand impersonation, credential harvesting active

2Brand Impersonation Scanner

Searches the domain database for domains impersonating a given brand using typosquatting, homoglyph substitution, and similar-name detection algorithms. Scrapes discovered domains for brand asset misuse including stolen logos, copied layouts, and trademark infringement.

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MCP Tool Definition
Domain DB Web Scraping GPT-4o Vision Fuzzy Match
brand_impersonation_scanner brand_domain: string — Legitimate brand domain to protect (e.g. "wellsfargo.com") brand_name: string — Brand name for text matching (e.g. "Wells Fargo") scan_depth: string — "quick" (DB only) or "full" (DB + scrape + visual comparison)
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AI Processing Pipeline
PROCESSING PIPELINE ════════════════════════════════════════════════════════════ Step 1: Generate typosquatted variants (swap, insert, delete, homoglyph) Step 2: Search 100M domain DB for matching domains across all TLDs Step 3: Filter results by registration date, PageRank, and category anomalies Step 4: Scrape discovered domains and screenshot homepages Step 5: Run GPT-4o Vision to detect brand asset misuse (logos, colors, layout) Step 6: Cross-reference with known brand asset hashes for exact match detection Step 7: Classify threat level and generate takedown recommendation report
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Example Output
MCP RESPONSE — brand_impersonation_scanner ════════════════════════════════════════════════════════════ wellsfargo.com | Brand: "Wells Fargo" | Scan: full IMPERSONATION DOMAINS FOUND: 47 active threats CRITICAL THREATS (active credential harvesting): wellsfarg0.com — CRITICAL | Age: 5 days | Homoglyph: o→0 Logo: 99.1% match | Login form: Active phishing | Hosting: RU wells-fargo-secure.net — CRITICAL | Age: 2 days | Pattern: brand+keyword Layout: Cloned login page | Form target: External API | Hosting: UA weilsfargo.com — CRITICAL | Age: 8 days | Typo: ll→il Full site clone: Scraping live site content | Hosting: CN HIGH THREATS (brand misuse, no active harvesting yet): wellsfargo-rewards.com — HIGH | Age: 14 days Reward survey scam using brand logo and colors wellsfargosupport.org — HIGH | Age: 21 days Fake customer support portal collecting account details SUMMARY: Critical: 3 domains | High: 8 domains | Medium: 14 domains | Low: 22 domains New in last 7 days: 6 domains Hosting concentration: RU (12), CN (9), UA (7), US (19) RECOMMENDATION: Immediate takedown for 3 critical domains — UDRP filing recommended

3Fake E-Commerce Identifier

Uses AI vision to screenshot and analyze e-commerce sites for fraud indicators including stolen product images, unrealistic pricing, missing contact information, absent return policies, no real customer reviews, and suspicious payment flows.

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MCP Tool Definition
Web Scraping GPT-4o Vision Domain DB Image Analysis
fake_ecommerce_identifier domain: string — E-commerce domain to analyze (e.g. "super-deals-outlet.shop") product_category: string — Expected product category for price benchmarking check_images: boolean — Reverse image search product photos for stolen assets (default: true)
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AI Processing Pipeline
PROCESSING PIPELINE ════════════════════════════════════════════════════════════ Step 1: Query domain DB for age, PageRank, IAB category, and country Step 2: Scrape homepage, product pages, /about, /contact, /returns, /reviews Step 3: Screenshot key pages and analyze layout quality via GPT-4o Vision Step 4: Check product images for reverse-image matches against legitimate retailers Step 5: Compare pricing against market averages (flag >70% discounts) Step 6: Verify contact info, physical address, return policy, and social proof Step 7: Score fraud likelihood and classify: LEGITIMATE / SUSPICIOUS / FRAUDULENT
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Example Output
MCP RESPONSE — fake_ecommerce_identifier ════════════════════════════════════════════════════════════ super-deals-outlet.shop | Category: Electronics FRAUD SCORE: 91/100 (FRAUDULENT) DOMAIN SIGNALS: Domain age: 12 days — registered 2026-02-18 PageRank: 0.0/10 — zero web authority IAB Category: Not categorized in 100M DB PRICING ANALYSIS: MacBook Pro M4: $299 (retail $2,499) — 88% below market iPhone 16 Pro: $149 (retail $1,199) — 88% below market Sony WH-1000XM6: $39 (retail $349) — 89% below market All 47 products priced 80-92% below retail — statistically impossible IMAGE ANALYSIS: Product images: 38 of 47 images traced to Amazon/BestBuy listings Lifestyle photos: Stock photos from Shutterstock (watermarks cropped) TRUST SIGNALS MISSING: Contact page: No phone number, email is generic @gmail.com Physical address: Address maps to empty lot in Nevada Return policy: Generic template text, contradicts itself Reviews: All 5-star, identical sentence structures, posted same day PAYMENT FLOW: Checkout: Only accepts wire transfer and cryptocurrency No PCI-compliant payment processor detected VERDICT: FRAUDULENT — Classic fake e-commerce scheme, block merchant immediately

4Merchant Category Verifier

Scrapes merchant websites and uses AI to verify that the actual business category matches the claimed Merchant Category Code (MCC). Flags mismatches that may indicate MCC fraud, miscoding for lower interchange rates, or deliberate category manipulation.

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MCP Tool Definition
Web Scraping GPT-4o Domain DB MCC Registry
merchant_category_verifier merchant_domain: string — Merchant website domain to verify claimed_mcc: string — Claimed MCC code (e.g. "5411" for Grocery Stores) merchant_name: string — Registered merchant name for cross-reference
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AI Processing Pipeline
PROCESSING PIPELINE ════════════════════════════════════════════════════════════ Step 1: Look up merchant domain in 100M DB for IAB category and metadata Step 2: Scrape homepage, /about, /products, /services pages Step 3: Send content to GPT-4o to classify actual business category Step 4: Map AI-detected category to corresponding MCC code Step 5: Compare claimed MCC vs. detected MCC — flag mismatches Step 6: Cross-reference IAB category from domain DB as third validation point Step 7: Calculate mismatch severity and interchange rate impact
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Example Output
MCP RESPONSE — merchant_category_verifier ════════════════════════════════════════════════════════════ quickmart-groceries.com | Claimed MCC: 5411 (Grocery Stores) VERIFICATION: MISMATCH DETECTED CLAIMED CATEGORY: MCC 5411: Grocery Stores, Supermarkets Interchange rate: 1.15% (regulated grocery rate) AI-DETECTED ACTUAL CATEGORY: Primary: Online Gambling / Casino (confidence: 94%) Secondary: Sports Betting Platform (confidence: 87%) Correct MCC: 7995 (Gambling Transactions) Interchange rate: 3.40% (high-risk category) DOMAIN DB CROSS-REFERENCE: IAB Category: IAB9-7 (Card Games) — confirms gambling PageRank: 1.2/10 | Country: CW (Curacao) | Age: 89 days EVIDENCE: Homepage content: "Sports betting", "live casino", "slot machines" Product pages: Casino games catalog, no grocery products found Payment methods: Crypto, e-wallets — inconsistent with grocery INTERCHANGE IMPACT: Rate difference: 2.25% — saving merchant $22,500 per $1M processed Estimated monthly volume: $340K | Monthly fraud savings: $7,650 VERDICT: MCC FRAUD — Gambling site registered as grocery for lower interchange rates

5Domain Typosquatting Finder

Generates all plausible typosquatted variants of a target domain (character swaps, insertions, deletions, homoglyphs, TLD variations) and checks which ones exist in the 100M domain database. Scrapes found domains to assess threat level and intent.

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MCP Tool Definition
Domain DB Web Scraping GPT-4o DNS Lookup
domain_typosquatting_finder target_domain: string — Domain to protect (e.g. "bankofamerica.com") variant_types: array — ["swap","insert","delete","homoglyph","tld","hyphen","subdomain"] scrape_found: boolean — Scrape and classify discovered domains (default: true)
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AI Processing Pipeline
PROCESSING PIPELINE ════════════════════════════════════════════════════════════ Step 1: Generate all typosquat permutations (typically 2,000-5,000 variants) Step 2: Batch-query 100M domain DB for matches across all generated variants Step 3: DNS lookup on matches to confirm active resolution Step 4: Scrape active domains — extract content, forms, and redirects Step 5: Classify intent via GPT-4o: phishing / ad parking / competitor / benign Step 6: Rank threats by proximity to target domain and content maliciousness
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Example Output
MCP RESPONSE — domain_typosquatting_finder ════════════════════════════════════════════════════════════ bankofamerica.com | Variants generated: 4,218 | Found in DB: 83 THREAT SUMMARY: 83 typosquat domains detected PHISHING (Active credential harvesting): bankofarnerica.com — CRITICAL | Homoglyph: m→rn Age: 4 days | Clone of login page, form POSTs to 91.x.x.x bankofarnerica.com — CRITICAL | Swap: me→rne Age: 6 days | SSL phishing kit detected, captures OTP codes bankofamerica-secure.com — CRITICAL | Hyphen+keyword Age: 1 day | Redirect chain → credential harvesting page AD PARKING / MONETIZATION (12 domains): bankoamerica.com — MEDIUM | Delete: f removed Parked page with financial services ads | Capturing mistyped traffic bankofamerica.net — MEDIUM | TLD variation Redirect to loan comparison affiliate site COMPETITOR / BRAND ABUSE (6 domains): bankofamerica-reviews.com — MEDIUM Fake review site promoting competitor banking products VARIANT BREAKDOWN: Swap: 14 found | Insert: 8 | Delete: 11 | Homoglyph: 19 | TLD: 22 | Hyphen: 9 Active phishing: 7 | Monetization: 12 | Benign/Parked: 64 PRIORITY: 7 domains require immediate takedown — legal templates attached

6Payment Page Security Analyzer

Screenshots payment and checkout pages and uses AI vision to assess security indicators including SSL badge presence, PCI compliance markers, suspicious redirects, mixed content warnings, and payment processor legitimacy verification.

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MCP Tool Definition
Web Scraping GPT-4o Vision SSL Check Domain DB
payment_page_security_analyzer checkout_url: string — URL of payment/checkout page to analyze follow_redirects: boolean — Follow and map redirect chains (default: true) check_pci: boolean — Verify PCI DSS compliance indicators (default: true)
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AI Processing Pipeline
PROCESSING PIPELINE ════════════════════════════════════════════════════════════ Step 1: Navigate to checkout URL and map full redirect chain Step 2: Screenshot final payment page at multiple viewport sizes Step 3: Analyze SSL certificate type (DV/OV/EV), issuer, and chain validity Step 4: GPT-4o Vision scans for PCI badges, trust seals, and security markers Step 5: Inspect form fields for proper encryption and tokenization Step 6: Check for mixed content, external scripts, and suspicious iframes Step 7: Verify payment processor against known legitimate gateway domains
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Example Output
MCP RESPONSE — payment_page_security_analyzer ════════════════════════════════════════════════════════════ cheapflights-booking.com/checkout SECURITY SCORE: 18/100 (DANGEROUS) REDIRECT CHAIN: cheapflights-booking.com/checkout → pay.cheapflights-booking.com/form → 185.234.xx.xx/collect.php (raw IP — no domain) SSL ANALYSIS: Certificate: DV certificate from Let's Encrypt (free, no identity verification) Expected: EV certificate for payment pages (shows company name) Mixed content: 3 external scripts loaded over HTTP (not HTTPS) PCI COMPLIANCE: PCI DSS badge: Image present but links to dead URL — likely fake badge Card form: No iframe isolation — card data handled by merchant JS Tokenization: None detected — raw card numbers sent in POST body PAYMENT PROCESSOR: Claimed: "Verified by Visa / Mastercard SecureCode" Actual: No recognized payment gateway integration found Form action: Submits to offshore IP, not a known PSP VISUAL ANALYSIS (AI Vision): Trust seals: Norton, McAfee badges — images only, not verified widgets Layout quality: Template-based, inconsistent branding Card logos: Visa/MC/Amex logos used without authorization VERDICT: UNSAFE — Fraudulent checkout page, card data at risk of theft

7Fake Review Site Detector

Scrapes review and testimonial pages and uses AI to detect fabricated patterns including identical review structures, stock photos used as reviewer avatars, unrealistic rating distributions, temporal anomalies, and generated text fingerprints.

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MCP Tool Definition
Web Scraping GPT-4o Image Analysis Domain DB
fake_review_site_detector domain: string — Website to analyze for fake reviews review_pages: array — Specific review page URLs (or auto-discover from /reviews, /testimonials) max_reviews: integer — Maximum reviews to analyze (default: 100)
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AI Processing Pipeline
PROCESSING PIPELINE ════════════════════════════════════════════════════════════ Step 1: Scrape review/testimonial pages and extract individual reviews Step 2: Parse review metadata: author name, date, rating, avatar image Step 3: Run GPT-4o text analysis for AI-generated content patterns Step 4: Reverse-image search reviewer avatars for stock photo matches Step 5: Analyze rating distribution (flag J-curve: mostly 5-star with few 1-star) Step 6: Detect temporal anomalies (bulk posting, suspicious intervals) Step 7: Calculate authenticity score and flag specific fabricated reviews
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Example Output
MCP RESPONSE — fake_review_site_detector ════════════════════════════════════════════════════════════ premium-forex-signals.com | Reviews analyzed: 87 AUTHENTICITY SCORE: 8/100 (FABRICATED) RATING DISTRIBUTION: 5-star: 84 reviews (96.6%) | 4-star: 2 | 3-star: 1 | 2-star: 0 | 1-star: 0 J-curve pattern: statistically impossible for legitimate business Expected distribution (organic): 5-star ~55%, 4-star ~25%, 3-star ~12% TEXT ANALYSIS (GPT-4o): AI-generated probability: 94% — detectable LLM patterns in 79 of 87 reviews Repeated phrases: "changed my life" (23x), "highly recommend" (31x) Sentence structure: 82% follow identical Subject-Adjective-Recommendation pattern Vocabulary diversity: 0.12 (legitimate reviews avg: 0.67) REVIEWER AVATARS: Stock photos: 71 of 87 avatars match stock photo databases AI-generated faces: 9 avatars detected as AI-generated (StyleGAN artifacts) Real photos: 7 appear legitimate but names don't match reverse lookup TEMPORAL PATTERNS: Posting dates: 43 reviews posted within same 48-hour window Interval: Reviews posted every 37 minutes (mechanical pattern) No reviews older than: 28 days (entire review history fabricated recently) VERDICT: FABRICATED — 94% fake review probability, merchant trust rating: F

8Credential Harvesting Scanner

Analyzes login pages using AI vision to detect credential harvesting attacks including fake login forms mimicking bank portals, suspicious form actions pointing to external servers, hidden form fields, and JavaScript keyloggers.

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MCP Tool Definition
Web Scraping GPT-4o Vision Domain DB JS Analysis
credential_harvesting_scanner target_url: string — Login page URL to analyze legitimate_domain: string — Legitimate domain being impersonated (e.g. "citibank.com") analyze_js: boolean — Deep-analyze JavaScript for keyloggers and exfiltration (default: true)
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AI Processing Pipeline
PROCESSING PIPELINE ════════════════════════════════════════════════════════════ Step 1: Navigate to target URL and screenshot the rendered login page Step 2: GPT-4o Vision compares visual layout against legitimate bank login Step 3: Extract all form elements, actions, methods, and hidden fields Step 4: Analyze JavaScript for keylogger patterns and data exfiltration calls Step 5: Check form POST targets against known malicious IP/domain lists Step 6: Query domain DB for hosting, registration, and category metadata Step 7: Generate threat assessment with evidence chain for incident response
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Example Output
MCP RESPONSE — credential_harvesting_scanner ════════════════════════════════════════════════════════════ citibank-onlineportal.com/login | Impersonating: citibank.com THREAT LEVEL: CRITICAL — Active Credential Harvester VISUAL COMPARISON (AI Vision): Layout similarity: 97.3% match to citibank.com/login Logo: Pixel-perfect copy of Citi logo and blue arc Color accuracy: #003DA5 — exact Citi brand blue Difference: Footer text slightly different, copyright year 2024 FORM ANALYSIS: Username field: name="user_id" — mimics Citi's actual field name Password field: name="password" autocomplete="off" Hidden fields: device_id, browser_fp, screen_res (fingerprinting) Form action: POST → api.citi-collect[.]ru/harvest.php 2FA prompt: Fake OTP page loads after credential submission JAVASCRIPT ANALYSIS: Keylogger: addEventListener('keydown') on password field — sends each keystroke Clipboard: Intercepts paste events to capture password manager entries Exfiltration: WebSocket to ws://185.xx.xx.xx:8443 — real-time data stream Anti-debug: Detects DevTools open, redirects to real Citi site DOMAIN INTELLIGENCE: Domain age: 1 day — registered today via Njalla (privacy registrar) Hosting: Bulletproof hosting in Moldova Not in domain DB: Too new, zero web presence INCIDENT RESPONSE: Block domain at network level, notify Citi CERT, report to registrar

9Social Engineering Site Classifier

Uses AI to classify website content for social engineering tactics including urgency language, fake security warnings, impersonation of government or law enforcement authorities, tech support scam patterns, and emotional manipulation techniques.

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MCP Tool Definition
Web Scraping GPT-4o Vision Domain DB NLP Analysis
social_engineering_site_classifier url: string — URL to classify for social engineering content tactics: array — ["urgency","authority","scarcity","fear","tech_support","impersonation"] include_popups: boolean — Capture and analyze modal/popup content (default: true)
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AI Processing Pipeline
PROCESSING PIPELINE ════════════════════════════════════════════════════════════ Step 1: Navigate to URL and wait for all dynamic content including popups Step 2: Screenshot page, popups, and any overlay modals separately Step 3: Extract all text content including countdown timers and alert messages Step 4: GPT-4o Vision analyzes visual scare tactics (fake virus alerts, warnings) Step 5: NLP analysis scores text for manipulation patterns per tactic category Step 6: Check for authority impersonation (FBI, IRS, Microsoft, bank logos) Step 7: Classify site and generate tactic-by-tactic breakdown with confidence scores
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Example Output
MCP RESPONSE — social_engineering_site_classifier ════════════════════════════════════════════════════════════ windows-security-alert.com/warning CLASSIFICATION: TECH SUPPORT SCAM (confidence: 98%) TACTICS DETECTED: Urgency: SEVERE — Countdown timer "Your PC will be locked in 4:59" Fear: SEVERE — "Trojan virus detected, bank credentials compromised" Authority: HIGH — Fake Microsoft logo, "Windows Defender Alert" Impersonation: HIGH — Uses Microsoft trademark and Windows UI elements VISUAL MANIPULATION (AI Vision): Fake dialog box: Simulated Windows error popup rendered in HTML/CSS Color scheme: Uses Microsoft red/yellow warning palette Background: Fake BSOD (Blue Screen of Death) behind popup Audio: Auto-playing alarm sound and robotic voice warning SCAM FLOW: Step 1: Fake virus scan animation (100% finding "threats") Step 2: "Call Microsoft Support: 1-888-XXX-XXXX" (premium-rate number) Step 3: Remote desktop session request if victim calls Browser lock: JavaScript prevents closing tab (fullscreen + beforeunload) LANGUAGE ANALYSIS: Urgency words: 47 instances ("immediately", "now", "urgent", "warning") Fear triggers: "identity theft", "bank account", "personal files deleted" Authority claims: "Microsoft Certified Technician", "Error #DW6VB36" DOMAIN INTELLIGENCE: Age: 2 days | PageRank: 0.0 | Country: IN IAB: Not categorized | Hosting: Shared hosting, rotating IPs ACTION: Block domain, report to Microsoft DART and FTC complaint database

10Fraud Network Domain Mapper

Uses the domain database to identify networks of related fraudulent domains sharing similar patterns — matching registration dates, hosting infrastructure, IAB categories, PageRank ranges, naming conventions, and WHOIS data to map organized fraud operations.

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MCP Tool Definition
Domain DB Web Scraping GPT-4o Graph Analysis
fraud_network_domain_mapper seed_domain: string — Known fraudulent domain to map outward from correlation_signals: array — ["hosting","registration","naming","category","pagerank","content"] max_depth: integer — Network mapping depth (default: 3 hops)
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AI Processing Pipeline
PROCESSING PIPELINE ════════════════════════════════════════════════════════════ Step 1: Query domain DB for seed domain metadata (hosting, registration, category) Step 2: Search DB for domains sharing hosting IP, name server, or registrar Step 3: Apply naming pattern matching (regex on domain name structure) Step 4: Scrape discovered domains and use GPT-4o to compare site templates Step 5: Build network graph — nodes=domains, edges=shared infrastructure Step 6: Identify cluster centers and calculate network reach/scale Step 7: Generate fraud network map with infrastructure takedown targets
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Example Output

Fraud Network Intelligence Report

SEED DOMAIN: fake-crypto-exchange.com ──────────────────────────────────────── Hosting: 185.234.xx.xx (Bulletproof Hosting, NL) | Registrar: Njalla Registration: 2026-01-12 | Name Pattern: [keyword]-crypto-[keyword].com NETWORK MAP: 127 related fraudulent domains identified CLUSTER 1 — Crypto Scam Ring (48 domains): fake-crypto-exchange.com → seed domain best-crypto-trading.com → same hosting, same template secure-crypto-wallet.net → same registrar, registered same day crypto-profit-now.com → identical CSS/JS fingerprint ... and 44 more | All share: same hosting block, same site template CLUSTER 2 — Fake Investment Platforms (34 domains): ai-stock-profits.com → same name server as Cluster 1 guaranteed-forex-returns.com → shared Google Analytics ID binary-options-elite.net → same WHOIS registrant email hash ... and 31 more | Connected to Cluster 1 via shared analytics and hosting CLUSTER 3 — Phishing Support Infrastructure (45 domains): customer-support-crypto.com → fake support portal for Cluster 1 verify-identity-secure.com → KYC document harvesting withdrawal-pending.net → exit scam delay page ... and 42 more | Role: credential/document harvesting for Clusters 1-2 NETWORK STATISTICS: Total domains: 127 | Hosting IPs: 4 (same /24 block) Registrars: 2 (Njalla, NameSilo) | Registration window: Jan 8-15, 2026 Shared template: 3 base templates across all 127 domains Estimated victims: Based on traffic estimates: 12,000-18,000/month TAKEDOWN TARGETS: 1. Hosting provider: 185.234.xx.0/24 — single block takedown eliminates 89% of network 2. Name server: ns1.frauddns.example — serves 112 of 127 domains 3. Registrars: Njalla (78 domains), NameSilo (49 domains) — abuse reports filed
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