Abstract
Network traffic classification is elementary to network security and management. Recent research tends to apply machine learning techniques to flow statistical feature based classification methods. The Gaussian Mixture Model (GMM) based on the correlation of flows has exhibited superior classification performance. It also has several important advantages, such as robust to distributional assumptions and adaption to any cluster shape. However, the performance of GMM can be severely affected by the number of clusters. In this paper, we propose the minimum description length (MDL) criterion which can balance the accuracy and complexity of the classification model effectively by evaluating the optimal number of clusters. We establish a new classification model and analyze its performance. A large number of experiments are carried out on two real-world traffic data sets to validate the proposed approach. The results demonstrate the efficiency of our approach.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Valenti S, Rossi D, Dainotti A et al (2013) Reviewing traffic classification. Data Traffic Monit Anal 123–147
Dainotti A, Pescape A, Claffy KC (2012) Issues and future directions in traffic classification. Network 35–40
Nguyen TTT, Grenville A (2008) A survey of techniques for internet traffic classification using machine learning. Commun Surv Tutorials 56–76
Wang Y, Xiang Y, Zhang J et al (2014) Internet traffic clustering with side information. J Comput Syst Sci 80:1021–1036
Celeux G, Chrétien S, Forbes F et al (2001) A component-wise EM algorithm for mixtures. J Comput Graph Stat 10
Zhang J, Xiang Y, Wang Y et al (2013) Network traffic classification using correlation information. Parallel Distrib Syst 24:104–117
Zhang J, Chen C, Xiang Y et al (2012) Semi-supervised and compound classification of network traffic. Distributed computing systems workshops (ICDCSW), pp 617–621
CAIDA. A day in the life of the internet. http://www.caida.org/projects/ditl/
Keys K, Moore D, Koga R et al (2001) The architecture of CoralReef: an internet traffic monitoring software suite. In: PAM
Lee S, Kim H, Barman D et al (2011) Netramark: a network traffic classification benchmark. ACM SIGCOMM Comput Commun Rev 41:22–30
Roughan M, Sen S, Spatscheck O et al (2004) Class-of-service mapping for QoS: a statistical signature-based approach to IP traffic classification. In: Proceedings of the 4th ACM SIGCOMM conference on internet measurement, pp 135–148
Williams N, Zander S, Armitage G (2006) A preliminary performance comparison of five machine learning algorithms for practical IP traffic flow classification. ACM SIGCOMM Comput Commun Rev 36:5–16
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer Science+Business Media Singapore
About this paper
Cite this paper
Zhao, Y., Chen, J., You, G., Teng, J. (2016). Network Traffic Classification Model Based on MDL Criterion. In: Park, J., Jin, H., Jeong, YS., Khan, M. (eds) Advanced Multimedia and Ubiquitous Engineering. Lecture Notes in Electrical Engineering, vol 393. Springer, Singapore. https://doi.org/10.1007/978-981-10-1536-6_1
Download citation
DOI: https://doi.org/10.1007/978-981-10-1536-6_1
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-1535-9
Online ISBN: 978-981-10-1536-6
eBook Packages: Computer ScienceComputer Science (R0)