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arXiv cs.AI ·
XAI-SDN: פלטפורמה פשוטה לזיהוי DDoS ב-SDN
XAI-SDN: An Explainable Entropy-Guided Machine Learning Framework for Real-Time DDoS Detection in Software Defined Networks
פלטפורמה פשוטה לזיהוי DDoS ב-SDN, המשתמשת באלגוריתם של רולינג וב-Random Forest עם SHAP TreeExplainer.
תקציר מקורי באנגליתarXiv: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 entropy-guided machine learning framework, called XAI-SDN, for real-time DDoS detection in SDN environments which is lightweight and explainable. The framework extends the flow features extracted by CICFlowMeter with eight Shannon entropy metrics obtained by an $\mathcal{O}(1)$ rolling algorithm and uses a Random Forest classifier with SHAP TreeExplainer for providing transparency at the prediction level. On a fixed temporal split, XAI-SDN achieves an accuracy of 99.9987\%, a macro F1-score of 99.9621\%, and an AU
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