Effective Feature Selection for 5G IM Applications Traffic Classification

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

doi:10.1155/2017/6805056

Abstract

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.

Highlights

Accurate network traffic classification is extremely important for the network management including IP network management, deploying quality of services (QoS)-aware mechanisms, monitoring security, bandwidth management, and intrusion detection
We proposed a feature selection algorithm to improve the performance of machine learning (ML)-based traffic classification technique and select effective features set for instant messaging (IM) applications traffic classification
Though the results of the five applied machine learning classifiers are different with respect to accuracy, recall, and precision using HIT Trace 1 dataset and NIMS dataset, some information can be obtained from experimental study for IM traffic classification: (i) From this study, it is clear that our proposed algorithm selects effective features set for IM traffic classification using two different network environment datasets in terms of classification accuracy, recall, and precision metrics

Summary

Introduction

Accurate network traffic classification is extremely important for the network management including IP network management, deploying QoS-aware mechanisms, monitoring security, bandwidth management, and intrusion detection. It is useful for the Internet Service Providers (ISPs), network operators, and network administrators to understand the traffic composition and prioritize some sensitive bandwidth traffic such as video conferencing and voice over IP (VoIP). Portbased technique was proposed, which is based on well-known port numbers for traffic classification. This technique is easy to be deployed and implemented. Moore and Papagiannaki [3] showed that port-based traffic classification technique does not give more than 50–70 percent accuracy

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