Why server-side is the only anti-cheat that matters
If your anti-cheat runs on the client, the player has the means to tamper with it.
This follows from how software works. Any code running on a machine the player controls can be dumped, reverse engineered, hooked and neutralized. The arms race between cheat developers and client-side anti-cheats has run for over a decade, and the cheat developers are ahead, because they have home-field advantage.
The client-side problem
Most anti-cheats ship a binary module to every player's machine. The module scans memory, checks processes and watches for injected DLLs. The cheat community responds with kernel-level cheats, hypervisor-based approaches and hardware-level input injection. The anti-cheat follows by asking for ring-0 access, running at boot and scanning the whole system.
Players are asked to trust all of it. They cannot read the code, see what it is doing or audit it. They install a closed module with kernel access and rely on it behaving as described.
For a large publisher that tradeoff can make sense, since they have the resources and the legal teams. A smaller studio, or a community running Garry's Mod or Minecraft, installs opaque, obfuscated binaries with no way to verify what they do. When those binaries break, the developer often puts the fault on the player.
The server-side approach
Server-side detection changes where the analysis happens. It works on the data the player sends to your server, so nothing has to be caught on a machine you do not control. Every aim event, movement tick and combat interaction already passes through the server, and the data is available to analyze.
When you process that data on the server, the player cannot interfere. They do not know what you are measuring, and they cannot hook your analysis or dump your model, because none of it runs on their machine. Cheating shows up in the data that the player produces while playing.
What makes server-side hard
Few anti-cheats take this route because it is difficult. Matching process lists and memory signatures is straightforward: you check known patterns against a database and flag hits. Reading player behavior from raw game data requires understanding what cheating looks like in the data, and that understanding has to generalize across different players, playstyles and skill levels.
A static threshold is not enough for this. If you set aimSpeed > 500, you either catch only the obvious rage cheats, which any admin can spot by hand, or you set the limit low enough to flag legitimate high-skill players. No single threshold cleanly separates a good player from a subtle cheater. The difference is a spectrum, and reading it takes a model that sees the whole picture.
For this reason we built ChrononLabs around machine learning. The model reads 182 general features and 68 tick-based sequence parameters per event. It was trained on thousands of real data points from active communities, covering actual cheaters and actual legitimate players, and it evaluates the whole shape of how a player aims, reacts and engages.
The transparency advantage
Detection runs on the server and the analysis runs on our infrastructure, so the integration only collects data. The code is unobfuscated and readable. You can read every line, adapt it to your server and verify exactly what it sends. The integration ships without DRM or private binary modules.
This is a deliberate choice. When you run someone else's code on your server, you should be able to read it.