Trustworthy Intrusion Detection: Confidence Estimation Using Latent Space

TL;DR

This paper proposes a new confidence estimation method using Variational Autoencoders to improve the reliability of intrusion detection systems in identifying malicious network activities.

Contribution

It introduces a latent space-based confidence metric for anomaly detection, enhancing IDS trustworthiness with a novel VAE approach.

Findings

Significant correlation (0.45) between reconstruction error and confidence metric

Improved anomaly detection accuracy on NSL-KDD dataset

Enhanced reliability of IDS predictions against cyberattacks

Abstract

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.