# Unsupervised Anomaly Detection in NSL-KDD Using β-VAE: A Latent Space and Reconstruction Error Approach

arXiv: https://arxiv.org/abs/2602.19785 · HTML: https://mlsanity.com/paper/2602.19785/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 17 references that are in the map.
- First posted on arXiv 23 Feb 2026.

## Builds on (most cited first, 5 of 5)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Efficient Out-of-Distribution Detection Using Latent Space of β-VAE for Cyber-Physical Systems](https://mlsanity.com/paper/2108.11800/) (2021)
- [Semi-Supervised Learning via Compact Latent Space Clustering](https://mlsanity.com/paper/1806.02679/) (2018)
- [Trustworthy Intrusion Detection: Confidence Estimation Using Latent Space](https://mlsanity.com/paper/2409.13774/) (2024)
- [Constricting Normal Latent Space for Anomaly Detection with Normal-only Training Data](https://mlsanity.com/paper/2403.16270/) (2024)

## Abstract (arXiv)

As Operational Technology increasingly integrates with Information Technology, the need for Intrusion Detection Systems becomes more important. This paper explores an unsupervised approach to anomaly detection in network traffic using β-Variational Autoencoders on the NSL-KDD dataset. We investigate two methods: leveraging the latent space structure by measuring distances from test samples to the training data projections, and using the reconstruction error as a conventional anomaly detection metric. By comparing these approaches, we provide insights into their respective advantages and limitations in an unsupervised setting. Experimental results highlight the effectiveness of latent space exploitation for classification tasks.