Residential IP proxies have some weaknesses. One is that they ofter change IP addresses during a single web session. Second, if IP come from the same proxies provider, they are often concentrated within a sing ASN, making them easier to detect.
We are working on an open‑source fraud prevention platform [1], and detecting fake users coming from residential proxies is one of its use cases.
The first blog post in this series[1], linked to at the top of TFA, offers an analysis on the potential of using ASNs to detect such traffic. Their conclusion was that ASNs are not helpful for this use-case, showing that across the 50k IPs they've blocked, there is less than 4 IP addresses per ASN, on average.
What was done manually in the first blog is exactly what tirreno helps to achieve by analyzing traffic, here is live example [1]. Blocking an entire ASN should not be considered a strategy when real users are involved.
Regarding the first post, it's rare to see both datacenter network IPs and mobile proxy IP addresses used simultaneously. This suggests the involvement of more than one botnet.
The main idea is to avoid using IP addresses as the sole risk factor. Instead, they should be considered as just one part of the broader picture of user behavior.
>One is that they ofter change IP addresses during a single web session. Second, if IP come from the same proxies provider, they are often concentrated within a sing ASN, making them easier to detect.
Both are pretty easy to mitigate with a geoip database and some smart routing. One "residential proxy" vendor even has session tokens so your source IP doesn't randomly jump between each request.
At least here in the US most residential ISPs have long leases and change infrequently, weeks or months.
Trying to understand your product, where is it intended to sit in a network? Is it a standalone tool that you use to identify these IPs and feed into something else for blockage or is it intended to be integrated into your existing site or is it supposed to proxy all your web traffic? The reason I ask is it has fairly heavyweight install requirements and Apache and PHP are kind of old school at this point, especially for new projects and companies. It's not what they would commonly be using for their site.
Indeed, if it's a real user from a residential IP address, in most cases it will be the same network. However, if it's a proxy from residential IPs, there could be 10 requests from one network, the 11th request from a second network, and the 12th request back from the same network. This is a red flag.
Thank you for your question. tirreno is a standalone app that needs to receive API events from your main web application. It can work perfectly with 512GB Postgres RAM or even lower, however, in most cases we're talking about millions of events that request resources.
It's much easier to write a stable application without dependencies based on mature technologies. tirreno is fairly 'boring software'.
Effective fraud prevention relies on both the full user context and the behavioral patterns of known online fraudsters. The key idea is that an IP address cannot be used as a red flag on its own without considering the broader context of the account.
However, if we know that the fraudsters we're dealing with are using mobile networks proxies and are randomly switching between two mobile operators, that is certainly a strong risk signal.
An awful lot of free Wi-Fi networks you find in malls are operated by different providers. Walking from one side of a mall to the other while my phone connects to all the Wi-Fi networks I’ve used previously would have you flag me as a fraudster if I understand your approach correctly.
We are discussing user behavior in the context of a web system. The fact that your device has connected to different Wi-Fi networks doesn't necessarily mean that all of them were used to access the web application.
Finally, as mentioned earlier, there is no silver bullet that works for every type of online fraudster. For example, in some applications, a TOR connection might be considered a red flag. However, if we are talking about hn visitors, many of them use TOR on a daily basis.
We are working on an open‑source fraud prevention platform [1], and detecting fake users coming from residential proxies is one of its use cases.
[1] https://www.github.com/tirrenotechnologies/tirreno