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Automatic Identification of Honeypot Server Using Machine Learning Techniques

The authors propose an automatic identification model based on a random forest algorithm with three feature groups—application-layer, network-layer, and other system-layer features—to remotely check whether a server runs a honeypot service. The experiment datasets were collected from public known platforms, and the authors report that the model achieved an AUC value of 0.93.

HoneypotNetworkApplication

What we know

The authors propose an automatic identification model based on a random forest algorithm with three feature groups—application-layer, network-layer, and other system-layer features—to remotely check whether a server runs a honeypot service. The experiment datasets were collected from public known platforms, and the authors report that the model achieved an AUC value of 0.93.

Evidence limits

Review limited to the Crossref-deposited abstract; full text was not read and results were not independently validated.

Reviewed: author abstract. Full-text results were not analyzed.

Sources and provenance

  1. Automatic Identification of Honeypot Server Using Machine Learning Techniques
    abstract · 2026-10-11

Reviewed: 2026-10-13. This record may change when new evidence is found.

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