Fine grained classification of Internet multimedia traffics

For the purposes of efficient network resource management and QoS (Quality of Service) support of different multimedia services, this paper proposes a fine grained classification scheme for Internet multimedia traffics using a novel low-complexity feature selection method based on coefficient of variation. We focus on web-browsing and network video services in this work. A number of QoS and network resource requirements related statistical features of these applications are studied and validated by their effectiveness in web-browsing and video traffic classification. This scheme classifies multimedia services with the combinations of these statistical features. Experiments are performed on a large scale real network multimedia traffic data. The results show that the proposed method can achieve better classification performance in contrast to existing methods.

[1]  Wenjun Wu,et al.  A Parallelized Network Traffic Classification Based on Hidden Markov Model , 2011, 2011 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery.

[2]  Paola Batistoni,et al.  International Conference , 2001 .

[3]  Manuela Pereira,et al.  Identification of Peer-to-Peer VoIP Sessions Using Entropy and Codec Properties , 2013, IEEE Transactions on Parallel and Distributed Systems.

[4]  Myung-Sup Kim,et al.  Real-time Classification of Internet Application Traffic using a Hierarchical Multi-class SVM , 2010, KSII Trans. Internet Inf. Syst..

[5]  Michael S. Borella,et al.  The effect of network delay and media on user perceptions of web resources , 2000, Behav. Inf. Technol..

[6]  Antonio Pescapè,et al.  Issues and future directions in traffic classification , 2012, IEEE Network.

[7]  Ítalo S. Cunha,et al.  Predicting the level of cooperation in a Peer-to-Peer live streaming application , 2016, Multimedia Systems.

[8]  Xiaochun Yun,et al.  Optimizing Traffic Classification Using Hybrid Feature Selection , 2008, 2008 The Ninth International Conference on Web-Age Information Management.

[9]  Vasaka Visoottiviseth,et al.  Classification of audio and video traffic over HTTP protocol , 2009, 2009 9th International Symposium on Communications and Information Technology.

[10]  Qusay H. Mahmoud,et al.  Real-time traffic classification for unified communication networks , 2013, 2013 International Conference on Selected Topics in Mobile and Wireless Networking (MoWNeT).

[11]  Jie Wu,et al.  Robust Network Traffic Classification , 2015, IEEE/ACM Transactions on Networking.

[12]  Rupesh Chandrakant Jaiswal,et al.  Machine learning based internet traffic recognition with statistical approach , 2013, 2013 Annual IEEE India Conference (INDICON).

[13]  Changcheng Huang,et al.  Classification of applications in HTTP tunnels , 2013, 2013 IEEE 2nd International Conference on Cloud Networking (CloudNet).

[14]  Antony Selvadoss Thanamani,et al.  Feature Selection Based on Information Gain , 2013 .

[15]  Christof Fetzer,et al.  Scalable Network Traffic Classification Using Distributed Support Vector Machines , 2015, 2015 IEEE 8th International Conference on Cloud Computing.

[16]  Yali Liu,et al.  Disambiguating HTTP: Classifying web Applications , 2011, 2011 7th International Wireless Communications and Mobile Computing Conference.

[17]  David D. Jensen,et al.  Privacy Vulnerabilities in Encrypted HTTP Streams , 2005, Privacy Enhancing Technologies.

[18]  Maurizio Dusi,et al.  Detecting HTTP Tunnels with Statistical Mechanisms , 2007, 2007 IEEE International Conference on Communications.

[19]  Hardeep Singh,et al.  Performance Analysis of Unsupervised Machine Learning Techniques for Network Traffic Classification , 2015, 2015 Fifth International Conference on Advanced Computing & Communication Technologies.

[20]  Surendra Byna,et al.  PANDA: Extreme Scale Parallel K-Nearest Neighbor on Distributed Architectures , 2016, 2016 IEEE International Parallel and Distributed Processing Symposium (IPDPS).

[21]  Brice Augustin,et al.  On Traffic Patterns of HTTP Applications , 2011, 2011 IEEE Global Telecommunications Conference - GLOBECOM 2011.