TY - GEN
T1 - Network traffic classification using machine learning algorithms
AU - Shafiq, Muhammad
AU - Yu, Xiangzhan
AU - Wang, Dawei
N1 - Publisher Copyright:
© 2018, Springer International Publishing AG.
PY - 2018
Y1 - 2018
N2 - Nowadays, Network Traffic Classification has got pivotal significance owing to high growth in the number of internet users. People use a variety of applications while browsing the pages of internet. It is very crucial for internet service providers (ISPs) to keep an eye on the network traffic. Most of the researches made on Network Traffic Classification, using Machine Learning Based Traffic Identification to collect data set from one campus network, don’t provide far better results. In this paper, we attempt to achieve highly precise results using different kinds of data sets and Machine Learning (ML) algorithms. We use two data sets, HIT and NIMS data sets for this work. Firstly, we capture online internet traffic of seven different kinds of applications such as DNS, FTP, TELNET, P2P, WWW, IM and MAIL to make data sets. Then, we extract the features of captured packets using NetMate tool. Thereafter, we apply three ML algorithms Artificial Neural Network, C4.5 Decision Tree and Support Vector Machine to compare the results of each algorithm. Experimental results show that all the algorithms give highly accurate results. But C4.5 decision tree algorithm provides 97.57% highly precise results when compared to other two machine learning algorithms.
AB - Nowadays, Network Traffic Classification has got pivotal significance owing to high growth in the number of internet users. People use a variety of applications while browsing the pages of internet. It is very crucial for internet service providers (ISPs) to keep an eye on the network traffic. Most of the researches made on Network Traffic Classification, using Machine Learning Based Traffic Identification to collect data set from one campus network, don’t provide far better results. In this paper, we attempt to achieve highly precise results using different kinds of data sets and Machine Learning (ML) algorithms. We use two data sets, HIT and NIMS data sets for this work. Firstly, we capture online internet traffic of seven different kinds of applications such as DNS, FTP, TELNET, P2P, WWW, IM and MAIL to make data sets. Then, we extract the features of captured packets using NetMate tool. Thereafter, we apply three ML algorithms Artificial Neural Network, C4.5 Decision Tree and Support Vector Machine to compare the results of each algorithm. Experimental results show that all the algorithms give highly accurate results. But C4.5 decision tree algorithm provides 97.57% highly precise results when compared to other two machine learning algorithms.
KW - Machine learning. IM application
KW - Network traffic classification
KW - WeChat traffic classification
UR - https://www.scopus.com/pages/publications/85033502602
U2 - 10.1007/978-3-319-69096-4_87
DO - 10.1007/978-3-319-69096-4_87
M3 - 会议稿件
AN - SCOPUS:85033502602
SN - 9783319690957
T3 - Advances in Intelligent Systems and Computing
SP - 621
EP - 627
BT - Advances in Intelligent Systems and Interactive Applications - Proceedings of the 2nd International Conference on Intelligent and Interactive Systems and Applications, IISA 2017
A2 - Xhafa, Fatos
A2 - Patnaik, Srikanta
A2 - Zomaya, Albert Y.
PB - Springer Verlag
T2 - 2nd International Conference on Intelligent and Interactive Systems and Applications, IISA 2017
Y2 - 17 June 2017 through 18 June 2017
ER -