Trustworthy Intrusion Detection: Confidence Estimation Using Latent Space
<|reference_start|>Trustworthy Intrusion Detection: Confidence Estimation Using Latent Space: 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.<|reference_end|>
@article{pitsiorlas2024trustworthy,
    title={Trustworthy Intrusion Detection: Confidence Estimation Using Latent
  Space},
    author={Ioannis Pitsiorlas, George Arvanitakis, Marios Kountouris},
    journal={arXiv preprint arXiv:2409.13774},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.13774},
    primaryClass={cs.CR cs.AI cs.LG}
}