Effective Feature Selection for 5G IM Applications Traffic Classification

Joint Authors

Yu, Xiangzhan
Shafiq, Muhammad
Laghari, Asif Ali
Wang, Dawei

Source

Mobile Information Systems

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