A Survey on Feature Selection Techniques for Internet Traffic Classification

K-Nearest Neighbour (K-NN) is one of the popular classification algorithm, in this research K-NN use to classify internet traffic, the K-NN is appropriate for huge amounts of data and have more accurate classification, K-NN algorithm has a disadvantages in computation process because K-NN algorithm calculate the distance of all existing data in dataset. Clustering is one of the solution to conquer the K-NN weaknesses, clustering process should be done before the K-NN classification process, the clustering process does not need high computing time to conqest the data which have same characteristic, Fuzzy C-Mean is the clustering algorithm used in this research. The Fuzzy C-Mean algorithm no need to determine the first number of clusters to be formed, clusters that form on this algorithm will be formed naturally based datasets be entered. The Fuzzy C-Mean has weakness in clustering results obtained are frequently not same even though the input of dataset was same because the initial dataset that of the Fuzzy C-Mean is less optimal, to optimize the initial datasets needs feature selection algorithm. Feature selection is a method to produce an optimum initial dataset Fuzzy C-Means. Feature selection algorithm in this research is Principal Component Analysis (PCA). PCA can reduce non significant attribute or feature to create optimal dataset and can improve performance for clustering and classification algorithm. The resultsof this research is the combination method of classification, clustering and feature selection of internet traffic dataset was successfully modeled internet traffic classification method that higher accuracy and faster performance.

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Toward an efficient and scalable feature selection approach for internet traffic classification

Computer Networks ◽

10.1016/j.comnet.2013.04.005 ◽

2013 ◽

Vol 57 (9) ◽

pp. 2040-2057 ◽

Cited By ~ 44

Author(s):

Adil Fahad ◽

Zahir Tari ◽

Ibrahim Khalil ◽

Ibrahim Habib ◽

Hussein Alnuweiri

Keyword(s):

Feature Selection ◽

Internet Traffic ◽

Traffic Classification ◽

Selection Approach ◽

Internet Traffic Classification ◽

Feature Selection Approach

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Application-based feature selection for Internet traffic classification

2010 22nd International Teletraffic Congress (lTC 22) ◽

10.1109/itc.2010.5608734 ◽

2010 ◽

Cited By ~ 6

Author(s):

Taoufik En-Najjary ◽

Guillaume Urvoy-Keller ◽

Marcin Pietrzyk ◽

Jean-Laurent Costeux

Keyword(s):

Feature Selection ◽

Internet Traffic ◽

Traffic Classification ◽

Internet Traffic Classification ◽

Selection For

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Feature Selection Technique Impact for Internet Traffic Classification Using Naïve Bayesian

Jurnal Teknologi ◽

10.11113/jt.v72.4112 ◽

2015 ◽

Vol 72 (5) ◽

Cited By ~ 1

Author(s):

Tony Antonio ◽

Adi Suryaputra Paramita

Keyword(s):

Feature Selection ◽

Principal Component ◽

Internet Traffic ◽

The Internet ◽

Traffic Classification ◽

Feature Selection Technique ◽

Selection Technique ◽

Naive Bayesian ◽

Naïve Bayesian ◽

Internet Traffic Classification

Feature selection technique has an important role for internet traffic classification. This technique will present more accurate data and more accurate internet traffic classification which will provide precise information for bandwidth optimization. One of the important considerations in the feature selection technique that should be looked into is how to choose the right features which can deliver better and more precise results for the classification process. This research will compare feature selection algorithms where the Internet traffic has the same correlation that could fit into the same class. Internet traffic dataset will be collected, formatted, classified and analyzed using Naïve Bayesian. Formerly, the Correlation Feature Selection (CFS) is used in the feature selection to find a collection of the best sub-sets data from the existing data but without the discriminant and principal of a body dataset. We plan to use Principal Component Analysis technique in order to find discriminant and principal feature for internet traffic classification. Moreover, this paper also studied the process to fit the features. The result also shows that the internet traffic classification using Naïve Bayesian and Correlation Feature Selection (CFS) have more than 90% accuracy while the classification accuracy reached 75% for feature selection using Principal Component Analysis (PCA).

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