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HYBRID CYBER DECEPTION FRAMEWORK FOR PROACTIVE AND INTELLIGENT NETWORK

The authors present the Hybrid Cyber Deception System (HCDS), described as a Python-developed proactive tool that combines honeypots and honeytokens in a dynamically adaptive mode to simulate realistic services and redirect attackers in controlled, instrumented settings. The system integrates a deception-driven alerts correlation engine built on Python, Pandas and Scikit-learn, along with Moving Target Defense principles for continuous modulation of network parameters and decoy distribution, and was tested in a simulated environment with Windows and Ubuntu servers to profile attackers and detect complex attack vectors.

HoneypotHoneytokenDecoyMoving target defenseNetworkEndpoint

What we know

The authors present the Hybrid Cyber Deception System (HCDS), described as a Python-developed proactive tool that combines honeypots and honeytokens in a dynamically adaptive mode to simulate realistic services and redirect attackers in controlled, instrumented settings. The system integrates a deception-driven alerts correlation engine built on Python, Pandas and Scikit-learn, along with Moving Target Defense principles for continuous modulation of network parameters and decoy distribution, and was tested in a simulated environment with Windows and Ubuntu servers to profile attackers and detect complex attack vectors.

Evidence limits

Review limited to the Crossref deposited abstract; the full text was not read and the results, software and system effectiveness were not independently validated.

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

Sources and provenance

  1. HYBRID CYBER DECEPTION FRAMEWORK FOR PROACTIVE AND INTELLIGENT NETWORK
    abstract · 2026-10-11

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

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