DOD attack detection using ML
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Distributed Denial-of-Service (DDoS) attack detection using machine learning (ML) has been extensively researched. Various ML models have been proposed for identifying DDoS incidents. One approach involves training and testing an attack detection model using the XGBoost Classifier and Random Forest algorithms . Another approach utilizes a multilayer convolutional neural network (CNN) combined with grey relational analysis classifiers to screen attack levels in a decentralized wireless network . Additionally, an automated detection approach using the Extra Tree classifier, Decision Tree, XGBoost, and Random Forest has been proposed for cloud-based DDoS attack detection . In the context of software-defined networks (SDN), a solution called DataPlane-ML has been developed, which uses machine learning techniques for attack detection and a reputation-based mitigation solution to avoid blocking legitimate traffic . These approaches demonstrate the effectiveness of ML in detecting and mitigating DDoS attacks in various network environments.
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| Papers (5) | Insight |
|---|---|
16 Dec 2022 | The paper proposes an automated detection approach using machine learning techniques to effectively detect DDoS attacks. |
1 Citations | The paper does not specifically mention DOD attack detection using ML. The paper proposes DataPlane-ML, an integrated solution for detecting and mitigating DDoS attacks in SDN using machine learning techniques. |
| The provided paper does not specifically mention DOD (Denial of Defense) attack detection using ML (Machine Learning). | |
| The paper proposes a machine learning model-based approach for detecting DDoS attacks, specifically using the XGBoost Classifier and Random Forest algorithms. | |
| The paper proposes a machine learning model-based approach for detecting DDoS attacks, specifically using the XGBoost Classifier and Random Forest algorithms. |
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