How To Defeat VPN Location-Spoofing By Mapping Network Delays (thestack.com)
An anonymous reader writes: An interesting paper from a PhD student in Ontario outlines a system which in initial tests has proved 97% effective at unmasking geo-spoofing VPN users. The Client Presence Verification (CPV) system presented in the paper utilises analysis of delays in network packets in order to determine the user's location, disregarding the IP address geolocation information which currently underpins the efforts of content providers such as Netflix to prevent VPN users accessing content which is not licensed in their country. The detection system was tested at global network laboratory PlanetLab using 80 network nodes based in the U.S. and Canada.
I haven't RTFA yet, but If the analysis is solely based on network delays, then a VPN company could simply introduce randomized delays to all it's users, even the local ones. Then an analysing service wouldn't be able to definitively say whether any given user is geo-spoofing or not. The best they could say is that the connecting service is likely a VPN.
False positives are a pretty major issue when you look at Netflix's user base, 97% effective isn't very good if you're going to refuse to serve content to over a million paying customers every day.
If you think someone isn't free to have a different definition of "freedom" you may be a tyrant.
People have pointed out that this is hard to make because you can’t make signals move FTL. Basically, you can send a packet, and by the rules of TCP, the ACK is generated at the destination, so while you could artificially lengthen the round-trip ping time, you can’t shorten it. But why not? How about we have the VPN buffer the TCP packets and break the rules. When a packet is received from Netflix, the VPN sends the ACK. When the user’s computer sends its ACK, the VPN consumes it. If there’s a chance of this being unreliable, them’s the breaks.
97% to detect irregular behavior is completely useless unless the rate of regular and irregular behavior is reasonably balanced. In most commercial settings the rate is biased towards regular behavior by several orders of magnitude. In other words, thousands of times more more biased than 97:3.
Therefore, this system will have orders of magnitude more false positives than positives. So the positives will just disappear inside a mass of angry customers.
In short; the ratio of success has to be in the same order of magnitude as the ratio of irregular behavior. e.g.: for Netflix you'd need better than 99.99% precision.
Here, I must disagree. I'm a software developer and network engineer. Specifically, my particular software development specialty involves interacting intimately with the network layer. (I'm in the VoIP world.) These people are doing good work in relating characteristics of latency to distance and geolocation and along the way are learning a great deal about the various factors that influence latency and jitter in the real world across working, real world networks. While you may not enjoy the particular aims that they're pursuing as a commercialization strategy, they have to get paid somehow... Meanwhile, the things that they learn about the causes of latency, jitter, and other aspects of service quality in packet networking can be USEFULLY utilized by everyone else in improving the network. Just a thought.