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Zot Cell
7 starsRust
I Replaced ML Anomaly Detection with Artificial Immune System in Rust
by nkr_hn·Mar 1, 2026·1 point·1 comment
AI Analysis
●●●BangerWizardryBig BrainZero to One
Immune system anomaly detection beats ML without training data, dependencies, or GPU.
Strengths
- •Novel biological algorithm (kinetic proofreading + Darwinian receptor selection) replaces statistical ML entirely
- •Zero external dependencies and single-file Rust implementation — ships anywhere without build chains
- •Nanosecond-level timing probes across three independent channels (memory latency, clock contention, allocator pressure) catch real hardware stress without instrumentation
Weaknesses
- •Tested only on macOS Apple Silicon; unclear generalization to x86, Linux, or sustained production workloads
- •Benchmark data is promising (93–95% accuracy) but sample size and threat diversity not fully disclosed
Category
Target Audience
Systems engineers, DevOps, or security teams monitoring for computational threats on resource-constrained hardware
Similar To
Prometheus (metrics-based anomaly detection) · Falco (kernel-level threat detection) · scikit-learn Isolation Forest
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