---
license: mit
task_categories:
  - tabular-classification
tags:
  - anomaly-detection
  - time-series
  - time-series-classification
  - server-monitoring
  - cybersecurity
  - benchmark
  - waveguard
  - zero-training
pretty_name: WaveGuard Anomaly Detection Benchmarks
size_categories:
  - 1K<n<10K
---

# WaveGuard Anomaly Detection Benchmarks

Curated benchmark datasets and comparison results for evaluating anomaly detection models.
Includes labeled training (normal) and test (mixed normal + anomalous) splits, plus
head-to-head comparisons between WaveGuard and traditional methods.

## Benchmark Comparisons (`benchmark_results/`)

WaveGuard vs. IsolationForest, LOF, and OneClassSVM across 12 datasets.

**Summary: WaveGuard ranked #1 on all 12 datasets by F1 score.**

| Dataset | WaveGuard | IsolationForest | LOF | OneClassSVM | Winner |
|---------|:---------:|:---------------:|:---:|:-----------:|:------:|
| Credit Card Fraud* | **0.653** | 0.607 | 0.601 | 0.472 | WaveGuard |
| Network Intrusion* | **0.598** | 0.252 | 0.232 | 0.546 | WaveGuard |
| Crypto Fraud | **1.000** | 0.933 | 0.946 | 0.897 | WaveGuard |
| Prompt Injection | **0.976** | 0.952 | 0.976 | 0.889 | WaveGuard |
| Phish Guard | **0.976** | 0.905 | 0.952 | 0.816 | WaveGuard |
| Content Guard | **0.975** | 0.842 | 0.879 | 0.784 | WaveGuard |
| Fraud Lens | **0.949** | 0.896 | 0.882 | 0.800 | WaveGuard |
| Ad Click Fraud | **0.988** | 0.952 | 0.930 | 0.889 | WaveGuard |
| Insurance Claims | **0.972** | 0.921 | 0.959 | 0.833 | WaveGuard |
| Network Security | **0.990** | 0.962 | 0.980 | 0.952 | WaveGuard |
| API Monitoring | **0.959** | 0.909 | 0.933 | 0.814 | WaveGuard |
| Log Anomalies | **0.946** | 0.875 | 0.875 | 0.805 | WaveGuard |

*\*Real-world datasets. Others use domain-specific test suites with realistic feature schemas.*

See `benchmark_results/comparison.json` for full details including sample sizes,
feature counts, and anomaly rates.

## Datasets

### 1. Server Metrics (`server_metrics/`)

Simulated server health metrics with injected failure events.

- **Features**: cpu, memory, disk_io, network, errors (5 numeric)
- **Training**: 500 normal samples
- **Test**: 100 samples (15 anomalous)
- **Anomaly types**: CPU spike, memory leak, disk saturation, network flood

### 2. Synthetic Time Series (`synthetic_timeseries/`)

Controlled synthetic signals with known anomaly injection points.

- **Patterns**: sinusoidal, trend, seasonal, random walk
- **Anomaly types**: point (spike), contextual (subtle shift), collective (regime change)
- **Training**: 200 clean windows per pattern
- **Test**: 50 windows per pattern (10 anomalous each)

## Format

Each dataset is provided as Parquet files:

```
dataset_name/
  train.parquet     # Normal samples only
  test.parquet      # Mixed normal + anomalous
  metadata.json     # Feature descriptions, anomaly counts, creation params
```

## Usage

```python
from datasets import load_dataset

ds = load_dataset("emergentphysicslab/waveguard-benchmarks", "server_metrics")
train = ds["train"].to_pandas()
test = ds["test"].to_pandas()
```

## Evaluation Protocol

1. Train/fit your detector on `train.parquet` only
2. Score each row in `test.parquet`
3. Report: Precision, Recall, F1, AUC-ROC, Average Latency
4. Compare against WaveGuard baseline in the model card

## Citation

```bibtex
@dataset{waveguard_benchmarks2026,
  title={WaveGuard Anomaly Detection Benchmarks},
  author={Partin, Greg},
  year={2026},
  url={https://huggingface.co/datasets/emergentphysicslab/waveguard-benchmarks}
}
```