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- W4313339002 abstract "The main objective of the research work is to categories the network traffic using a count vectorizer with an improved accuracy rate by using Comparing the novel Support Vector Machine (SVM) to the Naive Bayes (NB) Classifier. The data set in this work utilizes the publicly available Kaggle network traffic data set and UCI machine learning repository. The sample size of classifying the network traffic with improved accuracy rate was sample 5000 (Group 1=2500 and Group 2 =2500) and the calculation is carried out utilizing G-power 0.8 with alpha and beta qualities are 0.05, 0.2 with a confidence interval at 95%. Classifying the network traffic using a count vectorizer with improved accuracy rate is carried out by Support Vector Machine (SVM), whereas various samples and Naive Bayes (NB) where the (N = 10) Sample size. The Novel Support Vector Machine (SVM) classifier has 92.123 higher accuracy rate when compared to the accuracy rate of Naive Bayes (NB) is 89.123. There exist a statistical major distinction between the two groups (p=0.013; p<0.05) with confidence interval 95%. Novel Support Vector Machine (SVM) provides better outcome inaccuracy rate with improved accuracy rate." @default.
- W4313339002 created "2023-01-06" @default.
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- W4313339002 date "2022-11-20" @default.
- W4313339002 modified "2023-09-26" @default.
- W4313339002 title "Mechanism of Network Traffic Detection using Count Vectorizer" @default.
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- W4313339002 doi "https://doi.org/10.1109/3ict56508.2022.9990673" @default.
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