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Real-Time Network Intrusion Detection and Active Honeypot Deployment Using Machine Learning on KDD-Based Traffic Classification

The paper describes a hybrid intrusion detection system combining a supervised classifier trained on the NSL-KDD dataset with a Docker-based honeypot that deploys automatically when suspicious activity is detected. Traffic is classified into five categories, with a voting ensemble of three classifiers producing the detection.

HoneypotNetwork

What we know

The paper describes a hybrid intrusion detection system combining a supervised classifier trained on the NSL-KDD dataset with a Docker-based honeypot that deploys automatically when suspicious activity is detected. Traffic is classified into five categories, with a voting ensemble of three classifiers producing the detection.

Evidence limits

Review based only on the abstract deposited with Crossref; the full text was not examined and the results were not independently validated.

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

Sources and provenance

  1. Real-Time Network Intrusion Detection and Active Honeypot Deployment Using Machine Learning on KDD-Based Traffic Classification
    abstract · 2026-10-11

Reviewed: 2026-10-16. This record may change when new evidence is found.

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