Detect cheaters by their gameplay behaviour

Gaming platforms detect cheaters by their gameplay behaviour

Needs setupAI Act: no extra obligationsFinding anomalies & fraudGames

Fits: Customer service · IT & software development

Aimbots, wallhacks and bots stand out through gameplay behaviour, even when the cheat software itself is new and unknown. Honest players get fairer rounds, and your support team has fewer reports to go through by hand.

Is this for you?

  • Your game has a competitive mode, leaderboards or player trading, where cheating harms others.
  • You store game data per round, such as hits, reaction times, movements and player reports, and you have confirmed cheater cases for comparison.
  • You have a team that looks at suspected cases and handles appeals.
First step today

Make up a table of twelve players of the same rank, including one who clearly stands out, a strong player on a new account and one where only a single value is out of line. Have it analysed with the template below and look at whom the analysis would wrongly suspect.

How to go about it

  1. Define cheat types and behavioural features
  2. Bring together round data, reports and known cases
  3. Set up detection rules and a behavioural model
  4. Have suspected cases reviewed by people
  5. Define sanctions and an appeals process
  6. Adapt features to new cheats

Templates to copy

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Analyse
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Plan the rollout
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Included in the training

The complete guide

  • 6 steps explaining exactly what to do
  • 3 proven templates to copy into your own AI
  • Safe use: data, checking, deciding, informing
  • Pitfalls, effort and how to recognise success

With the training you get all 430 guides for as long as your access runs.

See the trainingOne-off per person, no subscription

Same approach, other use cases

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GA-04 · Legal status 08.10.2026