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

Shafiq, Muhammad; Yu, Xiangzhan; Laghari, Asif Ali; Lu, Yao; Karn, Nabin Kumar; Abdesssamia, Foudil; Salahuddin,

doi:10.1109/hpcc-smartcity-dss.2016.0019

Cited by 20 publications

(12 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…There are researches that are focused on DPI in SDN [11], [12], [13], while others are focused on the security aspects of DPI [14], [15], often proposing an introduction of probes or SDN appliances within the network. Network traffic classification in traditional networks is researched in the works of [16] and [17], but they do not consider the use of ML algorithms in NFV environment. Vergara-Reyes et al [4] introduced an NFV environment in which different types of TCP traffic are generated.…”

Section: Related Workmentioning

confidence: 99%

Efficiency of Supervised Machine Learning Algorithms in Regular and Encrypted VoIP Classification within NFV Environment

Ilievski¹,

Latkoski²

2020

RADIOENGINEERING

View full text Add to dashboard Cite

Cloudification of all computing environments is an undergoing process. The process has overpassed the classical Virtual Machines (VM) and Software-Defined Networking (SDN) approach and has moved towards dockizing, microservices, app functions, network functions etc. 5G penetration is another trend, and it is built on such platforms. In this environment we are investigating the efficiency of supervised machine learning algorithms for classification of regular and encrypted Voice over IP (VoIP) traffic that 5G relies on, within a virtualized Network Functions Virtualization (NFV) environment and an east-west based network traffic. We are using statistical methods for classification of network packets without the need of inspecting the payload data and without the source, destination and port information of the packets. The efficiency is analyzed from a point of precision of the classification, but also from a point of time consumption, as adding delay to the original traffic may cause a problem, especially within 5G environments where packet delay is crucial.

show abstract

Section: Related Workmentioning

confidence: 99%

Efficiency of Supervised Machine Learning Algorithms in Regular and Encrypted VoIP Classification within NFV Environment

Ilievski¹,

Latkoski²

2020

RADIOENGINEERING

View full text Add to dashboard Cite

show abstract

“…We select 50 statistical flow-based features and got very effective accuracy results. Similarly, in [9,10], we classify IM traffic accurately and find out effective features for IM traffic classification using machine learning algorithms. However, more than 50 features increase computational complexity and decrease accuracy results.…”

Section: Mobile Information Systemsmentioning

confidence: 99%

Effective Feature Selection for 5G IM Applications Traffic Classification

Shafiq¹,

Yu²,

Laghari³

et al. 2017

Mobile Information Systems

Self Cite

View full text Add to dashboard Cite

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.

show abstract

“…Some authors have carried out works for classifying traffic in traditional networks by using supervised ML algorithms, but there is a bit of information about the behavior of these algorithms in NFV-based networks. Shafiq et al, (2016a;2016b); Singh et al, (2013); Zander and Armitage (2011); Bujlow et al, (2012); Qin et al, (2011); and Choudhury and Bhowal (2015) perform classifications of IP traffic in traditional networks using ML algorithms (e.g., Bayes Net, Decision Tree, Random Forest, and Naïve Bayes) to identify the most precise.…”

Section: Related Workmentioning

confidence: 99%

“…Traffic classification has been performed in traditional networks by using ML algorithms. The works by Shafiq et al, (2016a;2016b); Singh, Agrawal, and Sohi (2013); Zander and Armitage (2011); Bujlow, Riaz, and Pedersen (2012); Qin et al, (2011); and Choudhury and Bhowal (2015) reveal that the supervised ML algorithms have an adequate performance in traffic classification tasks. However, none of such works has focused on classifying traffic in NFV-based networks.…”

Section: Introductionmentioning

confidence: 99%

“…La clasificación de tráfico se ha realizado en redes tradicionales utilizando algoritmos de ML. Los trabajos realizados por Shafiq et al, (2016a;2016b); Singh, Agrawal y Sohi (2013); Zander y Armitage (2011); Bujlow, Riaz, y Pedersen (2012); Qin et al, (2011); y Choudhury and Bhowal (2015) indican que los algoritmos ML de aprendizaje supervisado presentan un desempeño adecuado en tareas de clasificación de tráfico. No obstante, ninguno de los anteriores trabajos se ha enfocado en la clasificación de tráfico en redes basadas en NFV.…”

Section: Introductionunclassified

See 1 more Smart Citation

A benchmarking of the efficiency of supervised ML algorithms in the NFV traffic classification

2017

View full text Add to dashboard Cite

The implementation of NFV allows improving the flexibility, efficiency, and manageability of networks by leveraging virtualization and cloud computing technologies to deploy computer networks. The implementation of autonomic management and supervised algorithms from Machine Learning [ML] become a key strategy to manage this hidden traffic. In this work, we focus on analyzing the traffic features of NFV-based networks while performing a benchmarking of the behavior of supervised ML algorithms, namely J48, Naïve Bayes, and Bayes Net, in the IP traffic classification regarding their efficiency; considering that such an efficiency is related to the trade-off between time-response and precision. We used two test scenarios (an NFV-based SDN and an NFV-based LTE EPC). The benchmarking results reveal that the Naïve Bayes and Bayes Net algorithms achieve the best performance in traffic classification. In particular, their performance corroborates a good trade-off between precision and time-response, with precision values higher than 80 % and 96 %, respectively, in times less than 1,5 sec.

show abstract

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

Cited by 20 publications

References 9 publications

Efficiency of Supervised Machine Learning Algorithms in Regular and Encrypted VoIP Classification within NFV Environment

Efficiency of Supervised Machine Learning Algorithms in Regular and Encrypted VoIP Classification within NFV Environment

Effective Feature Selection for 5G IM Applications Traffic Classification

A benchmarking of the efficiency of supervised ML algorithms in the NFV traffic classification

Contact Info

Product

Resources

About