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Innovative Malware Detection: Practical Swarm Optimization and fuzzyKNN Model in Honeypot Environment

The study proposes a malware detection system that combines Particle Swarm Optimization (PSO) with a fuzzyKNN model, using PSO to select a feature set from a malware dataset and employing a honeypot as a controlled environment for collecting samples and training the model. The authors compare their method against standard detection techniques and report that the PSO-fuzzyKNN approach enhances malware detection accuracy.

HoneypotEndpoint

What we know

The study proposes a malware detection system that combines Particle Swarm Optimization (PSO) with a fuzzyKNN model, using PSO to select a feature set from a malware dataset and employing a honeypot as a controlled environment for collecting samples and training the model. The authors compare their method against standard detection techniques and report that the PSO-fuzzyKNN approach enhances malware detection accuracy.

Evidence limits

Review based only on the Crossref-deposited abstract and bibliographic metadata; the full text was not consulted and results, reproducibility, or software were not independently validated.

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

Sources and provenance

  1. Innovative Malware Detection: Practical Swarm Optimization and fuzzyKNN Model in Honeypot Environment
    abstract · 2026-10-11

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

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