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• W4200029159 abstract "The number of attacks increased with speedy development in web communication in the last couple of years. The Anomaly Detection method for IDS has become substantial in detecting novel attacks in Intrusion Detection System (IDS). Achieving high accuracy are the significant challenges in designing an intrusion detection system. It also emphasizes applying different feature selection techniques to identify the most suitable feature subset. The author uses Extremely randomized trees (Extra-Tree) for feature importance. The author tries multiple thresholds on the feature importance parameters to find the best features. If single classifiers use, then the classifier's output is wrong, so that the final decision may be wrong. So The author uses an Extra-Tree classifier applied to the best-selected features. The proposed method is estimated on standard datasets KDD CUP'99, NSL-KDD, and UNSW-NB15. The experimental results show that the proposed approach performs better than existing methods in detection rate, false alarm rate, and accuracy." @default.
• W4200029159 created "2021-12-31" @default.
• W4200029159 creator A5017726772 @default.
• W4200029159 creator A5028093146 @default.
• W4200029159 date "2021-11-26" @default.
• W4200029159 modified "2023-09-27" @default.
• W4200029159 title "An Ensemble Approach for Feature Selection and Classification in Intrusion Detection Using Extra-Tree Algorithm" @default.
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• W4200029159 doi "https://doi.org/10.4018/ijisp.2022010113" @default.
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