ASN Lookup Accuracy Problems
I'm running a web tool called 'What is My ISP?' which aims to accurately identify the internet service provider for a given IP address. We primarily rely on IP-to-ASN mapping and subsequent WHOIS data to extract ISP information.
However, we're consistently running into accuracy issues, specifically around differentiating the actual last-mile ISP from transit providers, CDNs, or large cloud providers whose ASNs might show up for an end-user's IP. For instance, an IP might resolve to a major backbone provider's ASN, but the user is actually getting service from a much smaller, local ISP peering with that backbone. This is where our current ASN lookup methods fall short.
We've tried various approaches including aggregating data from multiple public IP geolocation and ASN lookup databases (MaxMind, IPinfo.io, etc.), performing reverse DNS lookups and parsing hostnames for ISP clues, cross-referencing WHOIS records for the ASN owner and IP block registrant, and attempting to deduce ISP from known IP ranges for major providers.
The problem is, these methods often give us the network operator of a larger segment or transit route, not the direct ISP providing internet access to the end-user. This is particularly challenging for residential IPs or IPs behind VPNs and proxies where the immediate ASN doesn't reflect the true service provider. Our current ASN lookup accuracy just isn't cutting it for the end-user ISP.
What advanced methodologies, data sources, or algorithms are out there to improve the accuracy of end-user ISP identification beyond the standard ASN lookup and WHOIS parsing? Are there specific BGP routing insights or less common data feeds that could help us pinpoint the actual ISP more reliably?
Help a brother out please...
2 Answers
Malik Osei
Answered 13 hours agoI hear you on the complexities of accurate end-user ISP identification. It's a common hurdle when you're trying to build robust network intelligence tools. Before diving into solutions, just a quick note: you mentioned 'ASN lookup' and 'ASN Lookup' โ small detail, but for consistency, I'd usually stick with 'ASN lookup' in lowercase unless it's a proper noun. Just a thought!
Getting past the transit provider or CDN to the actual last-mile ISP requires moving beyond standard ASN and WHOIS data. Here are some advanced methodologies to consider:
- Deep BGP Path Analysis: Instead of just the immediate ASN, analyze the full AS-Path from public BGP routing tables. The origin AS (the last AS in the path) is often the actual end-user ISP, even if the IP block is advertised by an upstream transit provider. Tools like BGPStream, RIPEstat, or even direct access to BGP feeds can provide this granular data.
- Aggressive Reverse DNS Pattern Matching: While you're doing rDNS, focus on creating a sophisticated parser for common residential ISP naming conventions. Many last-mile ISPs follow predictable patterns (e.g.,
dhcp-*.city.isp.net,pool-*.customer.isp.com). Building a comprehensive regex library for these patterns can significantly improve accuracy. - Traceroute Data Integration: For specific, problematic IPs, running a traceroute from multiple global vantage points can reveal the actual hops and ASNs involved. The AS of the penultimate or antepenultimate hop is often the end-user ISP. While resource-intensive for every lookup, this data can inform heuristic rules and improve your IP geolocation logic.
- Specialized Commercial IP Intelligence APIs: Some premium providers go beyond basic geolocation. They often integrate real-time BGP data, traceroute insights, and proprietary algorithms to pinpoint the last-mile ISP. Look into services like Digital Element (NetAcuity), Neustar (now TransUnion), or even offerings from companies like Akamai (though their focus is usually CDN). These typically offer higher accuracy for a fee.
- Historical Data & Machine Learning: If you can build a dataset of known end-user IPs and their verified ISPs (perhaps through user feedback or manual verification), you can train a machine learning model. This model could learn to correlate various features (ASN, IP range, rDNS patterns, geographic data) to predict the true ISP more accurately than rules-based systems alone.
What kind of traffic volume are you handling currently, and how critical is real-time processing for your tool?
Aarti Yadav
Answered 6 hours agoMalik Osei, wow, 'Deep BGP Path Analysis' and 'Aggressive Reverse DNS Pattern Matching' are game changers. I'm gonna look into BGPStream and RIPEstat right away, hopefully that helps untangle this mess.