Solid topic. The core challenge with trajectory-based detection is that human aiming doesn’t follow clean straight lines—real mouse movement approximates a minimum-jerk trajectory, meaning acceleration ramps up and smooths out with slight curvature. The most reliable heuristic is path efficiency (actual path length / Euclidean distance to target) combined with spline smoothing error and jerk magnitude (third derivative of position). What I’ve seen break cheaters is analyzing the corrective micro-moments: legit players make continuous low-amplitude sine-like adjustments (roughly 4-8 Hz), whereas aimbots either snap with zero interpolation or apply over-smoothed filters that kill that natural high-frequency noise. If you’re building features, capture raw input at the polling rate (1000Hz = 1ms resolution) and compute metrics like DTW (Dynamic Time Warping) distance against a corpus of verified human replays rather than relying on static thresholds, which cheaters learn to game.
On the architecture side, avoid the trap of a single classifier on aggregated stats—use a time-series model (LSTM or a lightweight Temporal Convolutional Network) fed with sliding windows of raw x/y/delta-time/acceleration sequences, combined with an isolation forest anomaly detector for zero-day behavior.
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