HSAE: A Hybrid Unsupervised Autoencoder for Zero-Day Attack Detection in Realistic Environments

Fabiano C. da Silva, Franklin A. M. Venceslau, Rafael R. de Souza, José A. S. Monteiro

Abstract

Research Context: Anomaly detection in computer networks is essential for cybersecurity, especially in complex environments like corporate and IoT networks that face emerging threats. Scientific and/or Practical Problem: Traditional Intrusion Detection Systems (IDS) struggle to identify zero-day attacks, creating significant security gaps. Signature-based approaches fail against unknown threats, while supervised models require large volumes of labeled data rarely available in practice. U

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Cite This Resource

@article{llmsec202600205,
  title = {HSAE: A Hybrid Unsupervised Autoencoder for Zero-Day Attack Detection in Realistic Environments},
  author = {Fabiano C. da Silva and Franklin A. M. Venceslau and Rafael R. de Souza and José A. S. Monteiro},
  year = {2026},
  doi = {10.5753/sbsi.2026.248646},
  url = {https://doi.org/10.5753/sbsi.2026.248646},
}