Anti-cheat is not a one-time SDK install. It is an ongoing arms race with paid cheat engineers on the other side.
If your game has competitive multiplayer, players will cheat. If it has a marketplace or trading system, players will cheat for profit. Cheating is now an industry — paid subscription services, professionally engineered cheat clients, and a market that pays well. The defense has to be as serious as the offense.
Layer 1: server authority
The single most important defense. Never trust the client for anything that affects gameplay outcome. Movement, damage calculation, item drops, currency awards — all server-computed, all server-validated. Without this, every other layer is theater.
Layer 2: input validation
Even with server authority, the client sends inputs the server must validate. A player who claims to have aimed at coordinates impossibly fast, or moved through a wall, or fired faster than their weapon allows — the server rejects it. Cheaters fail loudly.
Layer 3: client integrity
For high-skill competitive games, kernel-level anti-cheat (Easy Anti-Cheat, Vanguard, BattlEye) is the table stakes. Detect modified clients, injected DLLs, debugger attachment, and known cheat signatures. The cost is real — kernel access requires explicit user trust and excludes some platforms.
Layer 4: behavioral detection
The most effective long-term layer. Train ML models on player input streams (mouse trajectories, decision timing, reaction patterns). Cheaters look statistically different from humans even when they fool the static detection. This is where Trust Factor (Valve), Recidivism (Activision), and Faceit's anti-cheat live.
Layer 5: economic friction
Make cheating expensive. Hardware bans, not just account bans. Phone number verification on competitive accounts. Trade restrictions on new accounts. The goal is not to make cheating impossible (it is not) — it is to make a banned account cost more than a new cheat subscription saves.
The realistic outcome
A serious anti-cheat program reduces cheating prevalence by 80-95%. The remaining cheaters are the cost of doing business. Treat detection latency as the metric — 24 hours is acceptable, 7 days is a problem, 30 days is a crisis.
What we ship
For competitive client projects, we install Easy Anti-Cheat on the client and a custom behavioral analysis pipeline on the server. The pipeline runs Python ML models against logged session data and queues bans for human review. Total false-positive rate target: under 0.1%.