[R] Using Darwinian selection instead of Neural Networks for anomaly detection.

About this article

I built ZOT as an experiment to see if biological immune systems (Darwinian selection + Kinetic Proofreading) could replace massive ML models for anomaly detection. It’s a 400+line, zero-dependency Rust binary that reads raw hardware telemetry (memory and clock latency) to autonomously evolve threat receptors. It hits ~95% accuracy in synthetic lab tests with near-zero false positives (it varies and needs further turning) I’ve taken the theory and the initial prototype as far as I can, but I ...