Anomaly Detection with Modern Neural Networks
Modern anomaly detection uses representation learning: neural networks learn a compact view of “normal” data, and anomalies stand out as reconstruction errors or low‑probability samples.
This post explains the main neural approaches and compares them to classical algorithms.
Modern neural approaches
1) Autoencoders
Train a network to reconstruct normal data. Anomalies produce larger reconstruction errors.
- Best for: high‑dimensional structured data (images, telemetry vectors).
- Tradeoff: can overfit and reconstruct anomalies if not tuned carefully.
2) Variational Autoencoders (VAE)
Like autoencoders, but with probabilistic latent space. Anomalies score low likelihood.
- Best for: uncertainty‑aware anomaly scoring.
- Tradeoff: more complex training and tuning.
3) GAN‑based detectors
A generator learns normal data; anomalies are samples that the discriminator rejects.
- Best for: image or signal data.
- Tradeoff: unstable training and sensitivity to mode collapse.
4) Sequence models (LSTM/Transformer)
Model time series and flag events with high prediction error.
- Best for: log streams, metrics, and event sequences.
- Tradeoff: needs a lot of clean normal data.
Neural vs classical (quick comparison)
| Dimension | Classical ML | Neural Networks |
|---|---|---|
| Data scale | Small to medium | Large‑scale |
| Interpretability | Higher | Lower |
| Feature engineering | Manual | Learned |
| Training cost | Low | High |
| Performance on high‑D data | Limited | Strong |
How to choose quickly
- Use classical methods if you need fast deployment, low compute, and clear explanations.
- Use neural methods if you have large datasets, high‑dimensional inputs, and can afford training cost.
- In practice, many teams use hybrids: classical filters for obvious anomalies + neural scoring for subtle cases.
Summary
Neural anomaly detection shines when data is complex and high‑dimensional, but it trades off interpretability and compute. Classical methods remain strong for quick, low‑cost detection and as guardrails around neural models.