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New AI framework enhances DDoS detection in Software Defined Networks

By PulseAugur Editorial · [1 sources] ·

Researchers have developed XAI-SDN, a new machine learning framework designed to detect distributed denial-of-service (DDoS) attacks in Software Defined Networks (SDN). This framework utilizes Shannon entropy metrics and a Random Forest classifier, enhanced with SHAP TreeExplainer for transparency. XAI-SDN demonstrates high accuracy, achieving nearly perfect scores in detection performance on the CIC-DDoS2019 benchmark, while maintaining efficient processing speeds. AI

IMPACT This framework offers improved real-time DDoS detection and transparency for Software Defined Networks.

RANK_REASON The item is a research paper detailing a new machine learning framework for network security. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New AI framework enhances DDoS detection in Software Defined Networks

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The item is a research paper detailing a new machine learning framework for network security. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Adeel Ahmad, Ali Akarma, Ahmad Ali, Hammad Muneer, Toqeer Ali Syed ·

    XAI-SDN: An Explainable Entropy-Guided Machine Learning Framework for Real-Time DDoS Detection in Software Defined Networks

    arXiv:2609.05701v1 Announce Type: cross Abstract: One of the biggest risks faced by Software Defined Networks (SDN) is the Distributed Denial of Service (DDoS) attack in which a compromised controller can make an entire network unusable. To address these challenges, we suggest an…

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