Fine-grained dissection of WeChat in cellular networks

Huang, Qun; Lee, Patrick P. C.; He, Caifeng; Qian, Jianfeng; Cheng, He Ming

doi:10.1109/iwqos.2015.7404750

Cited by 15 publications

(6 citation statements)

References 14 publications

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“…Although powerful, this approach is largely nonscalable and creates datasets that are challenging for public use due to privacy constraints. Other messaging apps, such as WeChat (Huang et al 2015), have been explored at scale although the focus has not been on the content and interactions. Instead, coarser analyses have been performed, e.g., size of messages.…”

Section: Studies Of Messagingmentioning

confidence: 99%

WhatApp Doc? A First Look at WhatsApp Public Group Data

Garimella

Tyson²

2018

ICWSM

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In this dataset paper we describe our work on the collection and analysis of public WhatsApp group data. Our primary goal is to explore the feasibility of collecting and using WhatsApp data for social science research. We therefore present a generalisable data collection methodology, and a publicly available dataset for use by other researchers. To provide context, we perform statistical exploration to allow researchers to understand what public WhatsApp group data can be collected and how this data can be used. Given the widespread use of WhatsApp, our techniques to obtain public data and potential applications are important for the community.

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Section: Studies Of Messagingmentioning

confidence: 99%

WhatApp Doc? A First Look at WhatsApp Public Group Data

Garimella

Tyson²

2018

ICWSM

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“…It is also important to classify WeChat messages, audio, and video call traffic accurately to manage quality of services (QoS). Huang et al [21] proposed measurement ChatDissect tool to measure WeChat application traffic and distinguish 150 K users and 16 GB traffic of WeChat from real-world network traces. In 2013, Church and De Oliveira [22] studied the performance of mobile instant messaging sending service with traditional short messages.…”

Section: Related Workmentioning

confidence: 99%

Effective Packet Number for 5G IM WeChat Application at Early Stage Traffic Classification

Shafiq

2017

Mobile Information Systems

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Accurate network traffic classification at early stage is very important for 5G network applications. During the last few years, researchers endeavored hard to propose effective machine learning model for classification of Internet traffic applications at early stage with few packets. Nevertheless, this essential problem still needs to be studied profoundly to find out effective packet number as well as effective machine learning (ML) model. In this paper, we tried to solve the above-mentioned problem. For this purpose, five Internet traffic datasets are utilized. Initially, we extract packet size of 20 packets and then mutual information analysis is carried out to find out the mutual information of each packet onnflow type. Thereafter, we execute 10 well-known machine learning algorithms using crossover classification method. Two statistical analysis tests, Friedman and Wilcoxon pairwise tests, are applied for the experimental results. Moreover, we also apply the statistical tests for classifiers to find out effective ML classifier. Our experimental results show that 13–19 packets are the effective packet numbers for 5G IM WeChat application at early stage network traffic classification. We also find out effective ML classifier, where Random Forest ML classifier is effective classifier at early stage Internet traffic classification.

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“…So, day to day increasing number of active users and traffic of this application can affect performance of the network. It is also important to classify WeChat messages and audio and video call traffic accurately to manage the quality of services (QoS) as Huang et al [29] proposed measurement ChatDissect tool to measure WeChat application traffic and distinguish 150K users and 16 GB traffic of WeChat from real-world network traces. In 2013, Church and Rodrigo de Oliveira [30] studied the performance of mobile instant messaging sending service with traditional short messages.…”

Section: Related Workmentioning

confidence: 99%

Effective Feature Selection for 5G IM Applications Traffic Classification

Shafiq¹,

Yu²,

Laghari³

et al. 2017

Mobile Information Systems

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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.

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Fine-grained dissection of WeChat in cellular networks

Cited by 15 publications

References 14 publications

WhatApp Doc? A First Look at WhatsApp Public Group Data

WhatApp Doc? A First Look at WhatsApp Public Group Data

Effective Packet Number for 5G IM WeChat Application at Early Stage Traffic Classification

Effective Feature Selection for 5G IM Applications Traffic Classification

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