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AI-Powered Intrusion Detection System with Honeypot Integration

The paper describes an intrusion detection system based on machine learning, enhanced by integrating the Cowrie honeypot to capture attacker behavioral data and thereby improve detection of indicators of compromise and attack patterns. The authors present an isolated multi-VM architecture, supervised and unsupervised models (Random Forest, Logistic Regression, Isolation Forest), a Flask API for real-time classification, and Kibana and Power BI dashboards, aiming to detect both known and unknown threats.

HoneypotNetwork

What we know

The paper describes an intrusion detection system based on machine learning, enhanced by integrating the Cowrie honeypot to capture attacker behavioral data and thereby improve detection of indicators of compromise and attack patterns. The authors present an isolated multi-VM architecture, supervised and unsupervised models (Random Forest, Logistic Regression, Isolation Forest), a Flask API for real-time classification, and Kibana and Power BI dashboards, aiming to detect both known and unknown threats.

Evidence limits

Review based solely on the Crossref-deposited abstract; the full text was not consulted and results were not independently validated.

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

Sources and provenance

  1. AI-Powered Intrusion Detection System with Honeypot Integration
    abstract · 2026-10-11

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

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