How does this even work? Why do mobile addresses have a high degree of trust from services? Why aren't they banned? And why in the case of bans not a proxy is a reason?

The mobile operator has a certain pool of ipv4 addresses. As a rule, there are about 14,000 addresses for one big city. The operator has at least 5,000,000 subscribers in the city, i.e. approximately 1250 people per IP address with an equitable distribution at a moment; in fact, the number of people using one address is much more. This technology is called GNAT, using its vulnerabilities and the lack of ipv4 addresses, we can hide “bots” in the crowd of real users and significantly reduce the risk of being blocked.

However, defective software can not support the work with backconnect-proxy, or not fully comply with the original social network client or service. For example, Instagram, constantly updates the protocol of the original client to deal with bots. However, the bot software does not release updates so quickly. This is exactly what happened to Instagram, when the likes from bots were detected as spam (when using regular, not mobile ipv6 / ipv4 proxies).

But not everything depends on the proxy and protocol. It is necessary to consider the average time, which the user spends in the social networks, the time of the day and the time zone. Let’s enumerate the factors affecting the detection and blocking of bots:

  1. Trust of the IP address (how many people use this address via the original app) – in this case there is nothing cooler than mobile proxies.
  2. The quality of the bot’s replication of the original application protocol. (The developers of bots do not always have time to fully study the changes in the protocol and quickly implement them).
  3. User-Agent, using mobile addresses you must have a mobile User-Agent and a complete copy of the protocol of the original social network client or website, like Avito. - this is the task of the software or browser (the application layer).
  4. Time of the day (Naturally, during the daytime social networks users are more active. Find out more about users' activity in social networks in the Internet).
  5. Time zone of the IP address and time zone of your system.
  6. Passive OS Fingerprint (Instagram is about phones, right? This means that the bot should be defined as a phone), we can simulate it, this is our task (the network layer).
  7. The frequency of changing the address on the account or changing the account. Just imagine how the address of an ordinary user changes during the day (most of the time it does not change, for example at work, and when going from home to work and back, it can change quite often, but with different time intervals).
  8. City and location in the account description and its correspondence to the IP-address.
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