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

A developer has open-sourced ZOT, an experimental anomaly detection system that draws inspiration from biological immune systems rather than traditional neural networks. The project uses principles of Darwinian selection and kinetic proofreading—mechanisms where immune cells evolve to recognize threats—to autonomously develop threat receptors by analyzing raw hardware telemetry like memory and clock latency. Built as a zero-dependency Rust binary under 500 lines of code, ZOT represents a radical departure from compute-intensive deep learning models, offering a lightweight alternative that the creator has taken as far as personal development allows and is now releasing under MIT license for community exploration.

The technical approach involves the system continuously evolving detection capabilities through simulated natural selection processes, achieving approximately 95% accuracy in synthetic laboratory tests with near-zero false positive rates, though the developer notes these results require further tuning. By reading low-level hardware signals directly, ZOT can potentially identify subtle anomalies that might evade conventional monitoring systems. The project's release includes both the functional codebase and an accompanying thesis document, inviting researchers and developers to fork, test, and potentially deploy the system in enterprise environments where its biological-inspired algorithms could provide more efficient and explainable threat detection compared to black-box neural networks.