Methodology & Scoring Architecture

proxyip.io is an independent network intelligence utility designed to evaluate public IP addresses from an anti-fraud, privacy, and proxy-detection perspective.

Rather than presenting a binary or opaque verdict, our platform synthesizes observable routing telemetry, public registry data, protocol-level signals, and client-side consistency checks into transparent probabilistic estimates.

All diagnostics are non-intrusive, privacy-respecting, and designed for network engineers, security auditors, researchers, and technical professionals.

Every analyzed IP is categorized into a defined infrastructure tier based on Autonomous System Number (ASN) allocations, Border Gateway Protocol (BGP) routing announcements, and Regional Internet Registry (RIR) records (ARIN, RIPE NCC, APNIC, LACNIC, and AFRINIC):

• Residential: End-user fixed broadband connections (FTTH, cable, xDSL) assigned to retail subscribers. These addresses generally carry the highest trust in commercial risk models.

• Datacenter / Cloud: IP blocks allocated to cloud infrastructure providers, VPS hosts, server farms, and colocation facilities. These are commonly used for hosting and automated scraping, resulting in elevated scrutiny by major web platforms.

• Mobile / Cellular: Mobile broadband carrier networks operating behind Carrier-Grade NAT (CGNAT) gateways for LTE/5G mobile subscribers. These exhibit high client rotation and shared addressing.

• Business / Enterprise: Dedicated corporate leased lines and static enterprise allocations, distinct from retail residential pools.

• Tor Exit Nodes: Verified active relays identified through real-time consensus directories published by directory authorities.

• Private / Local: Addresses residing in reserved IPv4/IPv6 blocks (RFC 1918, CGNAT 100.64/10, Loopback, Link-Local) that are not globally routable across the public Internet.

The Proxy / VPN likelihood score (0–100%) represents the estimated probability that the queried IP acts as an intermediary exit hop (commercial VPN, private proxy, datacenter tunnel, or open relay) rather than a direct subscriber endpoint.

Our heuristic engine evaluates multiple correlated vectors:

• Infrastructure Attribution: Presence in known hosting, colocation, or transit CIDRs rather than last-mile residential subnets.

• Active Anonymization Signatures: Direct overlap with public proxy lists, Tor directory authorities, and documented VPN server pools.

• Connection & Protocol Signatures: Telemetry hints such as Maximum Transmission Unit (MTU) sizing anomalies, atypical hop counts, and open tunnel ports commonly associated with WireGuard, OpenVPN, or SOCKS protocols.

Scores are stratified into qualitative bands: Low (≤25%), Medium (26–60%), and High (>60%) to assist in practical risk assessments.

Commercial web platforms deploy disparate anti-fraud and risk engines tailored to their threat models. A datacenter IP that easily streams video might be instantly challenged or blocked on a payment gateway.

We model expected block friction across four critical digital sectors:

• E-Commerce (e.g., Amazon, Shopify, Temu): Strict defenses against card testing, promotional abuse, and inventory botting. Datacenter and Tor IPs frequently encounter CAPTCHAs, checkout friction, or immediate order cancellation.

• Social Networks (e.g., TikTok, Instagram, Meta): Heavy emphasis on device reputation and residential IP authenticity to prevent mass account creation and engagement manipulation.

• AI Services (e.g., OpenAI, Claude, Gemini): Proactive rate-limiting and Cloudflare/WAF challenges on cloud hosting IP ranges to protect computational capacity against unauthenticated scraping.

• Financial & Payments (e.g., Stripe, PayPal, Wise): Stringent compliance and AML/fraud filters. Proxy and VPN exit hops are scrutinized heavily, and discrepancies between IP location and billing identity trigger automated holds.

Modern fraud detection systems do not evaluate the IP address in isolation; they analyze whether the browser environment harmonizes with the network location.

• WebRTC STUN Diagnostics: Evaluates ICE candidate gathering via standard STUN servers to detect whether a secondary public IPv4/IPv6 address or private LAN address leaks around an active proxy tunnel.

• DNS Leak Detection: Generates unique, cryptographic subdomains resolved by the client's system. By tracking the recursive resolver that queries the authoritative nameserver, we determine whether DNS traffic egresses through the expected tunnel or leaks to the user's local ISP.

• Browser Environment Consistency: Client-side heuristics verify that browser timezone, system UI language, HTTP Accept-Language headers, screen geometry, and WebGL rendering hardware align logically with the reported IP geolocation.

Internet routing topologies, BGP route advertisements, and IP block transfers occur continuously. Furthermore, commercial anti-fraud thresholds are dynamic, proprietary, and subject to regular recalibration.

All risk ratings, network categories, and block probabilities generated by proxyip.io are informational diagnostic estimates rather than absolute guarantees. They are provided without warranty to assist with security audits, network troubleshooting, and privacy posture verification.

proxyip.io is strictly an analytical testing utility and does not provide, host, or broker proxy or VPN services.