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- W2479602122 abstract "DoS attacks become a serious attack so as resource protection against this kind of attack is a compulsory task. The major challenge on designing detection scheme using machine learning technique is how to maximize detection rate with lower false alarm. In this paper, we employ and analyze the performance of multiple classifier system MCS to detect DoS attack. Several renowned base classifiers such as C4.5, SVM, and k-NN are combined using combination voting scheme and we compare the results with existing ensemble learning algorithms such as Bagging, Adaboost, and Rotation Forest. Based on the experiment using NSL-KDD dataset, MCS scheme has promising performance comparing to existing ensemble learner and single classifier." @default.
- W2479602122 created "2016-08-23" @default.
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- W2479602122 date "2016-01-01" @default.
- W2479602122 modified "2023-09-27" @default.
- W2479602122 title "Performance Analysis of Multiple Classifier System in DoS Attack Detection" @default.
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- W2479602122 doi "https://doi.org/10.1007/978-3-319-31875-2_28" @default.
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