README.md

metadata

title: WaveGuard - Physics-Based Anomaly Detection
emoji: 🌊
colorFrom: blue
colorTo: purple
sdk: gradio
sdk_version: 5.29.1
app_file: app.py
pinned: true
license: mit
tags:
  - anomaly-detection
  - time-series
  - physics
  - waveguard
  - zero-training
short_description: Zero-training anomaly detection using wave equation physics

WaveGuard: Physics-Based Anomaly Detection

Zero-training anomaly detection powered by wave equation physics. No neural networks. No gradient descent. No hyperparameter tuning. Deterministic.

Data enters a physics simulation where normal behavior creates smooth wave propagation. Anomalies disrupt the simulation in measurable ways -- catching threats that statistical methods miss.

Ranked #1 on all 12 benchmark datasets vs. IsolationForest, LOF, and OneClassSVM.

Demo

Click any scenario button to see pre-computed detection results from the actual WaveGuard engine:

• Time Series -- spikes, level shifts, flatlines (4/4 caught, 0% FP)
• Financial Fraud -- overseas transactions, structuring, card testing (4/4 caught, 0% FP)
• Process Health -- memory leaks, CPU saturation, crashes (4/4 caught, 1 marginal FP)
• Network Intrusion -- SYN floods, port scans, blackouts (3/3 caught, noisy FP)

Use WaveGuard on Your Data

Method	Link
RapidAPI (hosted)	Subscribe here -- Free tier available
Python SDK	pip install WaveGuardClient
MCP Server	Glama -- search "waveguard"
PyPI	pypi.org/project/WaveGuardClient
GitHub	github.com/gpartin/WaveGuardClient

How It Works

1. Encode -- data mapped into a simulation environment
2. Evolve -- wave equations adapt to your normal patterns
3. Lock -- evolved state frozen as the reference model
4. Test -- new data enters the simulation; anomalies create measurable disruptions
5. Score -- disruption magnitude determines the anomaly score with per-feature breakdown

Key Properties

• 2-15 training samples (vs hundreds for ML methods)
• Zero hyperparameters (sensitivity is the only knob)
• Deterministic (same input always gives same output)
• Explainable (per-feature scores show what triggered detection)
• CPU-friendly (GPU optional, not required)

Pricing (RapidAPI)

Plan	Price	Rate Limit
Free	$0	10 req/month
Pro	$0.005/req	60/min
Ultra	$0.003/req	300/min
Mega	$0.001/req	1000/min

WaveGuard v3.3.0 by Emergent Physics Lab