Trustworthy Intrusion Detection: Confidence Estimation Using Latent Space
This addresses the need for more trustworthy intrusion detection in network security, though it appears incremental as it builds on existing VAE methods.
The researchers tackled the problem of unreliable anomaly detection in Intrusion Detection Systems by developing a confidence metric based on Variational Autoencoder latent space representations, achieving a correlation of 0.45 between reconstruction error and their metric on the NSL-KDD dataset.
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.