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SD-Honeypot Integration for Mitigating DDoS Attack Using Machine Learning Approaches

The paper describes a honeypot workflow that attracts attacks by opening TCP/UDP ports for monitoring and detection within a software-defined-network DDoS mitigation proposal.

Honeypot

What we know

The paper describes a honeypot workflow that attracts attacks by opening TCP/UDP ports for monitoring and detection within a software-defined-network DDoS mitigation proposal.

Evidence limits

The review covers an institutional PDF excerpt and publisher metadata, not the full text. The system was not run and its effectiveness was not verified. Authors and topic overlap with a 2020 paper; family relation needs review at publication.

Reviewed: Public page. Full-text results were not analyzed.

Sources and provenance

  1. SD-Honeypot Integration for Mitigating DDoS Attack Using Machine Learning Approaches
    Public page · 2026-11-01

Reviewed: 2026-11-01. This record may change when new evidence is found.

RIS · BibTeX

Related resources

Paper2020

SDN-Honeypot Integration for DDoS Detection Scheme Using Entropy

Material reviewed: Abstract

The paper addresses detection and mitigation of DDoS attacks, specifically ICMP Flood, in Software Defined Networks (SDN), where controller centralization creates a vulnerability. The authors propose integrating the Modern Honey Network (MHN) into SDN and using a reactive application outside the controller based on entropy, computed from source IP, destination IP and protocol, to identify anomalous traffic.

NetworkHoneypot