Effective Feature Selection for 5G IM Applications Traffic Classification
Joint Authors
Yu, Xiangzhan
Shafiq, Muhammad
Laghari, Asif Ali
Wang, Dawei
Source
Issue
Vol. 2017, Issue 2017 (31 Dec. 2017), pp.1-12, 12 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2017-05-22
Country of Publication
Egypt
No. of Pages
12
Main Subjects
Telecommunications Engineering
Abstract EN
Recently, machine learning (ML) algorithms have widely been applied in Internet traffic classification.
However, due to the inappropriate features selection, ML-based classifiers are prone to misclassify Internet flows as that traffic occupies majority of traffic flows.
To address this problem, a novel feature selection metric named weighted mutual information (WMI) is proposed.
We develop a hybrid feature selection algorithm named WMI_ACC, which filters most of the features with WMI metric.
It further uses a wrapper method to select features for ML classifiers with accuracy (ACC) metric.
We evaluate our approach using five ML classifiers on the two different network environment traces captured.
Furthermore, we also apply Wilcoxon pairwise statistical test on the results of our proposed algorithm to find out the robust features from the selected set of features.
Experimental results show that our algorithm gives promising results in terms of classification accuracy, recall, and precision.
Our proposed algorithm can achieve 99% flow accuracy results, which is very promising.
American Psychological Association (APA)
Shafiq, Muhammad& Yu, Xiangzhan& Laghari, Asif Ali& Wang, Dawei. 2017. Effective Feature Selection for 5G IM Applications Traffic Classification. Mobile Information Systems،Vol. 2017, no. 2017, pp.1-12.
https://search.emarefa.net/detail/BIM-1189135
Modern Language Association (MLA)
Shafiq, Muhammad…[et al.]. Effective Feature Selection for 5G IM Applications Traffic Classification. Mobile Information Systems No. 2017 (2017), pp.1-12.
https://search.emarefa.net/detail/BIM-1189135
American Medical Association (AMA)
Shafiq, Muhammad& Yu, Xiangzhan& Laghari, Asif Ali& Wang, Dawei. Effective Feature Selection for 5G IM Applications Traffic Classification. Mobile Information Systems. 2017. Vol. 2017, no. 2017, pp.1-12.
https://search.emarefa.net/detail/BIM-1189135
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1189135