The core challenge with trajectory-based detection isn’t just catching linear flicks — it’s distinguishing the shape of human input from machine-generated input. Raw aimbot output tends to be suspiciously smooth along the correction curve, whereas human tracking exhibits micro-corrections, velocity-dependent overshoot, and non-linear acceleration profiles. The features that actually generalize well are things like the Jerk metric (third derivative of position), dwell time at the target, and the ratio of correction distance to total movement. I’d feed these into an ensemble — gradient boosting trees (XGBoost/LightGBM) tend to outperform raw neural nets here because they handle the small feature set and give you interpretable feature importance for tuning thresholds. A Kalman filter on the input stream can model expected vs. actual cursor position, and the residual error becomes a strong signal. One thing most people undersell: reaction time distribution. Humans cluster around 200-300ms with a right-skewed tail; aimbots fire in near-constant latency, so a simple histogram of time-to-acquire-target is surprisingly discriminating before you even get to ML.
For real-world architecture, I’d split detection across three tiers rather than relying on
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