Heuristic Machine Learning Aimbot Detection: Mouse Trajectory and Biometrics: Advanced Implementation and Real-World Architecture

[Dylan Evans]

So I’ve been prototyping a heuristic aimbot detector using mouse trajectory curvature analysis, and I’m hitting some real questions about the architecture. For the curvature metric specifically, are you computing instantaneous radius of curvature on the raw pointer stream, or is it worth smoothing first? I’m seeing a lot of noise from hand micro-tremors that’s muddying the jitter distribution, and I’m not sure whether a light low-pass filter or just modeling the tremor as a separate Gaussian component is the cleaner approach. What sampling rate are you running the capture at, and are you seeing aliasing issues below 250Hz?

On the inhuman angular acceleration tracking side, how are you handling the threshold tuning to avoid false positives on legit players with twitchy aim? I’m leaning toward a lightweight random forest on extracted features rather than dumping everything into a neural net, but I’m not settled. What feature set are you feeding the classifier, and how are you splitting the detection logic between userspace hooks and engine-level integration? Anybody here actually deployed this in production and have real numbers on detection latency versus false positive rates?

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