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Comprehensive analysis of network traffic data
Author(s) -
Miao Yuantian,
Ruan Zichan,
Pan Lei,
Zhang Jun,
Xiang Yang
Publication year - 2017
Publication title -
concurrency and computation: practice and experience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.309
H-Index - 67
eISSN - 1532-0634
pISSN - 1532-0626
DOI - 10.1002/cpe.4181
Subject(s) - principal component analysis , naive bayes classifier , random forest , computer science , k nearest neighbors algorithm , preprocessor , data mining , data set , decision tree , artificial intelligence , support vector machine , pattern recognition (psychology) , data pre processing , set (abstract data type) , programming language
Summary With the large volume of network traffic flow, it is necessary to preprocess raw data before classification to gain the accurate results speedily. Feature selection is an essential approach in preprocessing phase. The principal component analysis (PCA) is recognized as an effective and efficient method. In this paper, we classify network traffic flows by using the PCA technique together with 6 machine learning algorithms—Naive Bayes, decision tree, 1‐nearest neighbor, random forest, support vector machine, and H 2 O . We analyzed the impact of PCA on the classification results by applying each algorithm with and without PCA onto the data set. Experiments were set out by varying the size of input data sets, and the performances were measured from 2 aspects, including average overall accuracy and F‐measure. The computational time was also considered in analyzing the performance. Our results showed that random forest and 1‐nearest neighbor were the top 2 algorithms among all the 6 regarding the 2 metrics mentioned above. Then we continued the study of PCA impact on per class level with these 2 algorithms as examples. And the positive correlation between overall impact and the number of class with significant impact was revealed. Lastly, the visualization was used in exploring the reasons of the impacts caused by PCA. Two factors are considered in PCA's impact on per class level: benefit for classes grouped by PCA and mislabeled error interfered by nearby groups.