Why IP bans fail against modern bot farms (and what replaces them)
What IP bans were built for
A decade ago, a botting operation meant a rack of machines with a handful of static IPs. Ban those addresses and the farm went dark. The defense worked because the attacker's infrastructure was the bottleneck.
How modern bot farms dodge IP blocks
- Residential proxy networks. Operators rent access to millions of real residential IPs. Each bot session comes from a different home connection, often in the same city as your real players.
- Mobile IP rotation. Thousands of legitimate mobile players share single IPs through carrier-grade NAT, while bot clients hop between fresh mobile addresses constantly.
- Low-and-slow distribution. Farms run one bot per IP across a thousand addresses, so no single address looks abusive enough to trip a rate limit.
The collateral damage problem
Aggressive IP banning does not just miss bots; it hits players. University dorms, shared housing, internet cafes, and entire mobile carriers sit behind a small pool of addresses. Ban the address and you ban every legitimate player behind it, while the bots rotated an hour ago and never noticed.
What replaces the IP ban
- Behavioral identity. Score the client by how it behaves: input cadence, session patterns, and navigation paths. A bot on a fresh residential IP still moves like a bot.
- Account-level enforcement. Tie consequences to the account and its history, not the connection, so a flagged account stays flagged on any IP.
- Graduated friction. Route suspicious sessions into extra verification or throttled matchmaking instead of binary ban-or-allow. Bots see their economics collapse; false positives just see a CAPTCHA.
Where IP data still helps
IP reputation is not useless, but it is not an enforcement layer anymore. An IP from a known datacenter range raises suspicion, but no single IP should be the reason an account gets banned. The decision needs corroboration from behavior.
The migration path for live games
- Keep existing IP blocks in monitor mode to measure how many would have been false positives.
- Score sessions on behavioral signals alongside IP, and compare which catches the bots your team already knows about.
- Move enforcement to account-level actions with graduated friction first, hard bans only for corroborated cases.
- Review the IP list monthly. Anything older than a few weeks is probably banning someone else's customers.