Asif Ali Laghari

WeChat Text Messages Service Flow Traffic Classification Using Machine Learning Technique

In this era of information technology, network traffic classification is a very important and hot topic from the perspective of network security and management due to substantial use of dynamic applications. Numerous research models have been proposed in network traffic classification to classify different types of applications and achieve significant accuracy results. However, no work has been done to classify WeChat messages flow traffic. WeChat is a free instant messaging application. Hence, it is very important to classify WeChat text messages traffic. In this paper, we classify WeChat messages flows traffic using two different data sets, which are first captured using Wireshark tool from two different locations network environments, Harbin Institute of Technology Lab and Jinyuan Hotel and then 50 features are extracted from captured traffic. After that four machine learning algorithms SVM, C4.5, Bayes Net and nalve Byes are applied to classify the WeChat text messages traffic. Experimental results show that all classifiers give very high accuracy results using two different data sets. Using Jinyuan data set SVM and C4.5 decision tree algorithm give 100% accuracy result as compared to Bayes Net and Naive Bayes algorithm and using Harbin Institute of Technology Lab data set all classifiers give 99.7% high accuracy results.

WeChat Text and Picture Messages Service Flow Traffic Classification Using Machine Learning Technique

Network Traffic Classification carries great importance for both internet service providers (ISPs) and quality of services (QoSs) management. During the last two decades, a lot of machine learning models have been proposed and applied on different types of real time applications to classify their real time traffic and obtain very proficient accuracy results. However, no research has been done on WeChat text and picture messages traffic classification. In this paper, WeChat text and picture messages traffics are classified using two different types of datasets and 4 well-known machine learning algorithms. These two datasets, Harbin Institute of Technology (HIT) and Dorm13, are collected from two different network environments. Having captured the traffic 50 features, they are extracted respectively. Thereafter, well-known four machine learning algorithms C4.5 decision tree, Bayes Net, Naïve Bayes and SVM are used to classify WeChat text and picture messages traffic. Experimental result analysis show that using HIT data set all the applied machine learning classifiers classify WeChat text and picture messages traffic very accurately as compared to Dorm13 dataset. Using HIT dataset, all ML classifier perform very well, but C4.5 and SVM are the ones that give very effective accuracy results of 99.91% and 99.57% respectively as compared to other ML classifiers.

Quality of Experience Assessment of Video Quality in Social Clouds

Video sharing on social clouds is popular among the users around the world. High-Definition (HD) videos have big file size so the storing in cloud storage and streaming of videos with high quality from cloud to the client are a big problem for service providers. Social clouds compress the videos to save storage and stream over slow networks to provide quality of service (QoS). Compression of video decreases the quality compared to original video and parameters are changed during the online play as well as after download. Degradation of video quality due to compression decreases the quality of experience (QoE) level of end users. To assess the QoE of video compression, we conducted subjective (QoE) experiments by uploading, sharing, and playing videos from social clouds. Three popular social clouds, Facebook, Tumblr, and Twitter, were selected to upload and play videos online for users. The QoE was recorded by using questionnaire given to users to provide their experience about the video quality they perceive. Results show that Facebook and Twitter compressed HD videos more as compared to other clouds. However, Facebook gives a better quality of compressed videos compared to Twitter. Therefore, users assigned low ratings for Twitter for online video quality compared to Tumblr that provided high-quality online play of videos with less compression.

Effective Feature Selection for 5G IM Applications Traffic Classification

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.