# Trustworthy Intrusion Detection: Confidence Estimation Using Latent Space

arXiv: https://arxiv.org/abs/2409.13774 · HTML: https://mlsanity.com/paper/2409.13774/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 16 references that are in the map.
- First posted on arXiv 19 Sep 2024.

## Builds on (most cited first, 1 of 1)

- [A Latent Space Metric for Enhancing Prediction Confidence in Earth Observation Data](https://mlsanity.com/paper/2401.17342/) (2024)

## Built on by (most cited first, 3 of 3)

- [Agoran: An Agentic Open Marketplace for 6G RAN Automation](https://mlsanity.com/paper/2508.09159/) (2025)
- [A Conformal Predictive Measure for Assessing Catastrophic Forgetting](https://mlsanity.com/paper/2505.10677/) (2025)
- [Unsupervised Anomaly Detection in NSL-KDD Using β-VAE: A Latent Space and Reconstruction Error Approach](https://mlsanity.com/paper/2602.19785/) (2026)

## Abstract (arXiv)

This work introduces a novel method for enhancing confidence in anomaly detection in Intrusion Detection Systems (IDS) through the use of a Variational Autoencoder (VAE) architecture. By developing a confidence metric derived from latent space representations, we aim to improve the reliability of IDS predictions against cyberattacks. Applied to the NSL-KDD dataset, our approach focuses on binary classification tasks to effectively distinguish between normal and malicious network activities. The methodology demonstrates a significant enhancement in anomaly detection, evidenced by a notable correlation of 0.45 between the reconstruction error and the proposed metric. Our findings highlight the potential of employing VAEs for more accurate and trustworthy anomaly detection in network security.