Solving the App-Level Classification Problem of P2P Traffic Via Optimized Support Vector Machines
Abstract
Since the emergence of peer-to-peer (P2P) networking in the last 90s, P2P traffic has become one of the most significant portion of the network traffic. Accurate identification of P2P traffic makes great sense for efficient network management and reasonable utility of network resources. App-level classification of P2P traffic, especially without payload feature detection, is still a challenging problem. This paper proposes a new method for P2P traffic identification and app-level classification, which merely uses transport layer information. The method uses optimized support vector machines to perform large learning tasks, which is common in network traffic identification. The experimental results show that the proposed method has high efficiency and promising accuracy
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Traditional and multi-class SVM have been applied jointly to increase the accuracy of traffic classification and its applicability to large datasets.
“Similarly, traditional and multi-class SVM have been applied jointly to increase the accuracy of traffic classification and its applicability to large datasets [464].”
From A comprehensive survey on machine learning for networking: Evolution, applications and research opportunities (2018) · cites this paper as [464]
Cites
Cortes, C., & Vapnik, V. (1995) · Machine Learning · doi:10.1007/bf00994018 · Cited by 59,421 · see citing works
The Nature of Statistical Learning Theory
Vapnik, V. N. (1995) · doi:10.1007/978-1-4757-2440-0 · Cited by 39,138 · see citing works
Cortes, C., & Vapnik, V. N. (1995) · Machine Learning · doi:10.1023/a:1022627411411 · Cited by 34,565 · see citing works
A Tutorial on Support Vector Machines for Pattern Recognition
Burges, C. J. C. (1998) · Data Mining and Knowledge Discovery · doi:10.1023/a:1009715923555 · Cited by 16,437 · see citing works
An Introduction to Support Vector Machines and Other Kernel-based Learning Methods
Cristianini, N., & Shawe-Taylor, J. (2000) · doi:10.1017/cbo9780511801389 · Cited by 13,855 · see citing works
Support Vector Regression Machines
Drucker, H., Burges, C. J. C., Kaufman, L., Smola, A. J., & Vapnik, V. N. (1996) · Cited by 4,654 · see citing works
Making Large-Scale SVM Learning PracticalPreprint
Joachims, T. (1998) · RePEc: Research Papers in Economics · doi:10.17877/de290r-14262 · Cited by 4,514 · see citing works
(1999) · doi:10.1162/089976600300015565 · Cited by 2,799 · see citing works
Support Vector Method for Function Approximation, Regression Estimation, and Signal Processing
Vapnik, V. N., Golowich, S. E., & Smola, A. J. (1996) · Cited by 2,776 · see citing works
Support vector machine learning for interdependent and structured output spaces
Tsochantaridis, I., Hofmann, T., Joachims, T., & Altun, Y. (2004) · International Conference on Machine Learning · doi:10.1145/1015330.1015341 · Cited by 1,493 · see citing works
Accurate, scalable in-network identification of p2p traffic using application signatures
Sen, S. B., Spatscheck, O., & Wang, D. (2004) · International World Wide Web Conference (WWW) · doi:10.1145/988672.988742 · Cited by 807 · see citing works
Transport layer identification of P2P traffic
Karagiannis, T., Broido, A., Faloutsos, M., & claffy, K. (2004) · ACM Internet Measurement Conference (IMC) · doi:10.1145/1028788.1028804 · Cited by 708 · see citing works
Karagiannis, T., Broido, A., Brownlee, N., Claffy, K. C., & Faloutsos, M. (2005) · IEEE Global Telecommunications Conference, 2004. GLOBECOM '04. · doi:10.1109/glocom.2004.1378239 · Cited by 504 · see citing works
Roughan, M., Sen, S., Spatscheck, O., & Duffield, N. (2004) · Proceedings of the 4th ACM SIGCOMM conference on Internet measurement · doi:10.1145/1028788.1028805 · Cited by 494 · see citing works
Identifying Known and Unknown Peer-to-Peer Traffic
Constantinou, F. (2006) · IEEE International Symposium on Network Computing and Applications (NCA) · doi:10.1109/nca.2006.34 · Cited by 133 · see citing works
On the Learnability and Design of Output Codes for Multiclass Problems
Boros, E. (2002) · Machine Learning · Cited by 125 · see citing works
Towards Peer-to-Peer Traffic Analysis Using Flows
Kim, M., Kang, H.-J., & Hong, J. W. (2003) · Lecture notes in computer science · doi:10.1007/978-3-540-39671-0_6 · Cited by 33 · see citing works
Cross-Layer Peer-to-Peer Traffic Identification and Optimization Based on Active Networking
Dedinski, I., de Meer, H., Han, L., Mathy, L., Pezaros, D. P., Sventek, J., & Zhan, X. Y. (2009) · Lecture notes in computer science · doi:10.1007/978-3-642-00972-3_2 · Cited by 28 · see citing works
A Simple, Efficient and Flexible Approach to Measure Multi-protocol Peer-to-Peer Traffic
Bleul, H., & Rathgeb, E. P. (2005) · Lecture notes in computer science · doi:10.1007/978-3-540-31957-3_69 · Cited by 17 · see citing works
Pattern classification by convex analysis
Wernick, M. N. (1991) · Journal of the Optical Society of America A · doi:10.1364/josaa.8.001874 · Cited by 11 · see citing works
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Boutaba, R., Salahuddin, M. A., Limam, N., Ayoubi, S., Shahriar, N., Estrada-Solano, F., & Caicedo, O. M. (2018) · Journal of Internet Services and Applications · doi:10.1186/s13174-018-0087-2 · Cited by 1,036 · see citing works
- Authors
- Wang, R., Liu, Y., Yang, Y., & Zhou, X.
- Affiliations
- National University of Defense Technology (CN)
- Venue
- Sixth International Conference on Intelligent Systems Design and Applications
- Publisher
- IEEE
- Published
- 2006-10
- Volume
- 2
- Pages
- 534-539
- OpenAlex
- W2044798041
- Cited by
- 28
- References
- 20
- Citations by year
- 2022: 1 · 2021: 2 · 2020: 1 · 2019: 1 · 2018: 2 · 2014: 1 · 2013: 2 · 2012: 3
- FWCI
- 2.63
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- International Conference on Intelligent Systems Design and Applications (ISDA) (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Internet Traffic Analysis and Secure E-voting
- Physical Sciences › Computer Science › Computer Networks and Communications › Peer-to-Peer Network Technologies
- Physical Sciences › Computer Science › Computer Networks and Communications › Network Security and Intrusion Detection
- Keywords
- peer-to-peer traffic, P2P traffic identification, network traffic classification, network management
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