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THE ANALYSIS NETWORK FORENSICS USING HONEYPOT ON PUBLIC CLOUD COMPUTING SERVICE NETWORK

The work presents a dashboard visualization using honeypot log data from the AWN campus connected to IHP (Kemkominfo) Jakarta on a public cloud computing service network, aiming to categorize timestamps. The K-means clustering method is applied to categorize attacks into morning, noon, and night periods based on Western Indonesia Time and to identify ports 21, 80, 135, and 445 affected by DDoS attacks, with the stated expectation of supporting monitoring and anticipation of cloud network vulnerabilities.

HoneypotCloudNetwork

What we know

The work presents a dashboard visualization using honeypot log data from the AWN campus connected to IHP (Kemkominfo) Jakarta on a public cloud computing service network, aiming to categorize timestamps. The K-means clustering method is applied to categorize attacks into morning, noon, and night periods based on Western Indonesia Time and to identify ports 21, 80, 135, and 445 affected by DDoS attacks, with the stated expectation of supporting monitoring and anticipation of cloud network vulnerabilities.

Evidence limits

Review is limited to the publisher-deposited abstract in Crossref; the full text was not consulted and results, implementation, or effectiveness were not independently verified.

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

Sources and provenance

  1. THE ANALYSIS NETWORK FORENSICS USING HONEYPOT ON PUBLIC CLOUD COMPUTING SERVICE NETWORK
    abstract · 2026-10-11

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

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