Protocol oblivious classification of multimedia traffic

Voice and video over IP are becoming increasingly popular and represent the largest source of profits as consumer interest in online voice and video services increases, and as broadband deployments proliferate. In order to tap the potential profits that VoIP and IPTV offer, carrier networks have to efficiently and accurately manage and track the delivery of IP services. The traditional approach of using port numbers to classify traffic is infeasible due to the usage of dynamic port number. In this paper, we focus on a statistical pattern classification technique to identify multimedia traffic. Based on the intuitions that voice and video data streams show strong regularities in the packet inter-arrival times (IATs) and the associated packet sizes when combined together in one single stochastic process, we propose a system, called VOVClassifier, for voice and video traffic classification. VOVClassifier is an automated self-learning system that classifies traffic data by extracting features from frequency domain using Power Spectral Density (PSD) analysis and grouping features using Subspace Decomposition. We applied VOVClassifier to real packet traces collected from different network scenarios. Results demonstrate the effectiveness and robustness of our approach that is capable of achieving a detection rate of up to 100% for voice and 96.5% for video while keeping the false positive rate close to 0%. Copyright © 2009 John Wiley & Sons, Ltd.

[1]  John Wright,et al.  Segmentation of Multivariate Mixed Data via Lossy Data Coding and Compression , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[2]  Andrew W. Moore,et al.  Internet traffic classification using bayesian analysis techniques , 2005, SIGMETRICS '05.

[3]  Anirban Mahanti,et al.  Traffic classification using clustering algorithms , 2006, MineNet '06.

[4]  Sebastian Zander,et al.  Automated traffic classification and application identification using machine learning , 2005, The IEEE Conference on Local Computer Networks 30th Anniversary (LCN'05)l.

[5]  Panayiotis Mavrommatis,et al.  Identifying Known and Unknown Peer-to-Peer Traffic , 2006, Fifth IEEE International Symposium on Network Computing and Applications (NCA'06).

[6]  Michalis Faloutsos,et al.  BLINC: multilevel traffic classification in the dark , 2005, SIGCOMM '05.

[7]  Charles V. Wright,et al.  HMM Profiles for Network Traffic Classification (Extended Abstract) , 2004 .

[8]  Matthew Roughan,et al.  Class-of-service mapping for QoS: a statistical signature-based approach to IP traffic classification , 2004, IMC '04.

[9]  Renata Teixeira,et al.  Early application identification , 2006, CoNEXT '06.

[10]  Dario Rossi,et al.  Revealing skype traffic: when randomness plays with you , 2007, SIGCOMM '07.

[11]  John Wright,et al.  Segmentation of multivariate mixed data via lossy coding and compression , 2007, Electronic Imaging.

[12]  Sven Ehlert,et al.  Analysis and Signature of Skype VoIP Session Traffic , 2006 .

[13]  Sebastian Zander,et al.  Self-Learning IP Traffic Classification Based on Statistical Flow Characteristics , 2005, PAM.

[14]  Anthony McGregor,et al.  Flow Clustering Using Machine Learning Techniques , 2004, PAM.

[15]  Konstantina Papagiannaki,et al.  Toward the Accurate Identification of Network Applications , 2005, PAM.

[16]  Lindsay I. Smith,et al.  A tutorial on Principal Components Analysis , 2002 .

[17]  Sebastian Zander,et al.  A preliminary performance comparison of five machine learning algorithms for practical IP traffic flow classification , 2006, CCRV.

[18]  Anja Feldmann,et al.  An analysis of Internet chat systems , 2003, IMC '03.

[19]  Wei Hong.  Hybrid Models for Representation of Imagery Data , 2006 .

[20]  J. Tenenbaum,et al.  A global geometric framework for nonlinear dimensionality reduction. , 2000, Science.

[21]  Michalis Faloutsos,et al.  Transport layer identification of P2P traffic , 2004, IMC '04.

[22]  S T Roweis,et al.  Nonlinear dimensionality reduction by locally linear embedding. , 2000, Science.