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• W4360764547 abstract "Distributed denial-of-service (DDoS) attack is a malicious cybersecurity attack that has become a global threat. Machine learning (ML) as an advanced technology has been proven to be an effective way against DDoS attacks. Feature selection is a crucial step in ML, and researchers have put endless efforts to mitigate the “Curse of Dimensionality”. Feature selection is also causing problems to ML models, such as a decrease in prediction accuracy. Four supervised classification techniques, namely, Decision Tree (DT), k-Nearest Neighbors (KNN), Logistic Regression (LR), and Random Forest (RF), are tested using mutual information score ranking to study the necessity of feature selection in DDoS detection." @default.
• W4360764547 created "2023-03-25" @default.
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• W4360764547 date "2022-12-01" @default.
• W4360764547 modified "2023-10-16" @default.
• W4360764547 title "Curse of Feature Selection: a Comparison Experiment of DDoS Detection Using Classification Techniques" @default.
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• W4360764547 doi "https://doi.org/10.1109/ispa-bdcloud-socialcom-sustaincom57177.2022.00040" @default.
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