vTC: Machine Learning Based Traffic Classification as a Virtual Network Function
Abstract
Network flow classification is fundamental to network management and network security. However, it is challenging to classify network flows at very high line rates while simultaneously preserving user privacy. Machine learning based classification techniques utilize only meta-information of a flow and have been shown to be effective in identifying network flows. We analyze a group of widely used machine learning classifiers, and observe that the effectiveness of different classification models depends highly upon the protocol types as well as the flow features collected from network data.We propose vTC, a design of virtual network functions to flexibly select and apply the best suitable machine learning classifiers at run time. The experimental results show that the proposed NFV for flow classification can improve the accuracy of classification by up to 13%.
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The adaptive selection of features and classifiers in vTC, based on the flow protocol, results in an accuracy of 95.6%.
“The adaptive selection of features and classifiers in vTC, based on the flow protocol, result in an accuracy of 95.6%.”
From A comprehensive survey on machine learning for networking: Evolution, applications and research opportunities (2018) · cites this paper as [182]
Cites
Cortes, C., & Vapnik, V. (1995) · Machine Learning · doi:10.1007/bf00994018 · Cited by 59,421 · see citing works
Cortes, C., & Vapnik, V. N. (1995) · Machine Learning · doi:10.1023/a:1022627411411 · Cited by 34,565 · see citing works
A decision-theoretic generalization of on-line learning and an application to boosting
Freund, Y., & Schapire, R. E. (1997) · Journal of Computer and System Sciences · doi:10.1006/jcss.1997.1504 · Cited by 20,466 · see citing works
Springer Science+Business Media
Технология (2013) · Betascript Publishing eBooks · Cited by 3,087 · see citing works
Quinlan, J. R. (1987) · International Journal of Man-Machine Studies · doi:10.1016/s0020-7373(87)80053-6 · Cited by 2,553 · see citing works
An empirical study of the naive Bayes classifier
Rish, I. (2001) · Cited by 2,532 · see citing works
Lightweight DDoS flooding attack detection using NOX/OpenFlow
Braga, R., Mota, E. S., & Passito, A. (2010) · doi:10.1109/lcn.2010.5735752 · Cited by 726 · see citing works
Flow Clustering Using Machine Learning Techniques
McGregor, A., Hall, M., Lorier, P., & Brunskill, J. (2004) · Lecture notes in computer science · doi:10.1007/978-3-540-24668-8_21 · Cited by 521 · see citing works
Bayesian Neural Networks for Internet Traffic Classification
Auld, T., Moore, A. W., & Gull, S. F. (2007) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2006.883010 · Cited by 494 · see citing works
The UCI KDD archive of large data sets for data mining research and experimentation
Bay, S. D., Kibler, D. F., Pazzani, M. J., & Smyth, P. (2000) · ACM SIGKDD Explorations Newsletter · doi:10.1145/380995.381030 · Cited by 234 · see citing works
SDN-Based Application-Aware Networking on the Example of YouTube Video Streaming
Jarschel, M., Wamser, F., Hohn, T., Zinner, T., & Tran‐Gia, P. (2013) · European Workshop on Software Defined Networks (EWSDN) · doi:10.1109/ewsdn.2013.21 · Cited by 139 · see citing works
A Modular Machine Learning System for Flow-Level Traffic Classification in Large Networks
Jin, Y., Duffield, N. G., Erman, J., Haffner, P., Sen, S. B., & Zhang, Z. (2012) · ACM Transactions on Knowledge Discovery from Data · doi:10.1145/2133360.2133364 · Cited by 103 · see citing works
Qazi, Z. A., Lee, J., Jin, T., Bellala, G., Arndt, M., & Noubir, G. (2013) · ACM SIGCOMM Computer Communication Review · doi:10.1145/2534169.2491700 · Cited by 82 · see citing works
Ng, B., Hayes, M., & Seah, W. K. G. (2015) · IFIP Networking Conference (IFIP Networking) · doi:10.1109/ifipnetworking.2015.7145322 · Cited by 38 · see citing works
An efficient SVM-based method for multi-class network traffic classification
Jing, N., Yang, M., Cheng, S., Dong, Q., & Xiong, H. (2011) · IEEE International Performance, Computing, and Communications Conference · doi:10.1109/pccc.2011.6108074 · Cited by 34 · see citing works
Kruse, R., Borgelt, C., Klawonn, F., Moewes, C., Steinbrecher, M., & Held, P. (2013) · Texts in Computer Science · doi:10.1007/978-1-4471-5013-8 · Cited by 1 · 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
Toward Adaptive and Scalable OpenFlow-SDN Flow Control: A Survey
Alsaeedi, M., Mohamad, M. M., & Al-Roubaiey, A. A. (2019) · IEEE Access · doi:10.1109/access.2019.2932422 · Cited by 183 · see citing works
Machine Learning Meets Communication Networks: Current Trends and Future Challenges
Ahmad, I., Shahabuddin, S., Malik, H. H., Harjula, E., Leppänen, T., Lovén, L., Anttonen, A., Sodhro, A. H., Alam, M. M., Juntti, M. J., et al. (2020) · IEEE Access · doi:10.1109/access.2020.3041765 · Cited by 135 · see citing works
Machine learning‐based IDS for software‐defined 5G network
Li, J., Zhao, Z., & Li, R. (2017) · IET Networks · doi:10.1049/iet-net.2017.0212 · Cited by 91 · see citing works
In-Network Machine Learning Using Programmable Network Devices: A Survey
Zheng, C., Hong, X., Ding, D., Vargaftik, S., Ben-Itzhak, Y., & Zilberman, N. (2023) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2023.3344351 · Cited by 90 · see citing works
Mohammed, A. R., Mohammed, S., & Shirmohammadi, S. (2019) · IEEE International Symposium on Measurements & Networking (M&N) · doi:10.1109/iwmn.2019.8805044 · Cited by 83 · see citing works
Applications of Artificial Intelligence and Machine Learning in the Area of SDN and NFV: A Survey
Gebremariam, A. A., Usman, M., & Qaraqe, M. (2019) · International Multi-Conference on Systems, Signals & Devices (SSD) · doi:10.1109/ssd.2019.8893244 · Cited by 51 · see citing works
Sun, G., Chang, V. I. C., Yang, G., & Liao, D. (2017) · Information Sciences · doi:10.1016/j.ins.2017.08.021 · Cited by 48 · see citing works
Intelligent SDN Traffic Classification Using Deep Learning: Deep-SDN
Malik, A., de Fréin, R., Al-Zeyadi, M., & Andreu-Pérez, J. (2020) · International Conference on Computer Communication and the Internet (ICCCI) · doi:10.1109/iccci49374.2020.9145971 · Cited by 47 · see citing works
A Survey on Machine Learning Applications for Software Defined Network Security
Arevalo-Herrera, J., & Camargo, J. E. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-29729-9_4 · Cited by 29 · see citing works
iTeleScope: Softwarized Network Middle-Box for Real-Time Video Telemetry and Classification
Gharakheili, H. H., Lyu, M., Wang, Y., Kumar, H., & Sivaraman, V. (2019) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2019.2929511 · Cited by 28 · see citing works
A Survey on Requirements of Future Intelligent Networks: Solutions and Future Research Directions
Husen, A., Chaudary, M. H., & Ahmad, F. (2022) · ACM Computing Surveys · doi:10.1145/3524106 · Cited by 22 · see citing works
IP traffic classification in NFV: A benchmarking of supervised Machine Learning algorithms
Reyes, J. A. V., Ordoñez, M. C. M., Ordóñez, A., & Rendón, O. M. C. (2017) · IEEE Colombian Conference on Communications and Computing (COLCOM) · doi:10.1109/colcomcon.2017.8088199 · Cited by 17 · see citing works
Tonkal, Ö., & Polat, H. (2021) · Gazi Üniversitesi Fen Bilimleri Dergisi Part C Tasarım ve Teknoloji · doi:10.29109/gujsc.869418 · Cited by 13 · see citing works
A Novel SDN-Based Application-Awareness Mechanism by Using Deep Learning
Hu, N., Luan, F., Tian, X., & Wu, C. (2020) · IEEE Access · doi:10.1109/access.2020.3021185 · Cited by 11 · see citing works
A Survey of Privacy Protection and Network Security in User On-Demand Anonymous Communication
He, Y., Zhang, M., Yang, X., Luo, J., & Chen, Y. (2020) · IEEE Access · doi:10.1109/access.2020.2981517 · Cited by 8 · see citing works
Application of Artificial Intelligence and Machine Learning in Software Defined Networks
Ahmad, S. S., & Habelamateen, M. I. (2023) · Journal of Smart Internet of Things · doi:10.2478/jsiot-2023-0002 · Cited by 8 · see citing works
TeleScope: Flow-Level Video Telemetry Using SDN
Wang, Y., Orapinpatipat, C., Gharakheili, H. H., & Sivaraman, V. (2016) · European Workshop on Software Defined Networks (EWSDN) · doi:10.1109/ewsdn.2016.8 · Cited by 7 · see citing works
When NFV Meets ANN: Rethinking Elastic Scaling for ANN-based NFs
Zhang, M., Bai, J., Li, G., Meng, Z., Li, H., Hu, H., & Xu, M. (2019) · IEEE International Conference on Network Protocols (ICNP) · doi:10.1109/icnp.2019.8888133 · Cited by 5 · see citing works
Ilievski, G., & Latkoski, P. (2020) · Radioengineering · doi:10.13164/re.2020.0243 · Cited by 6 · see citing works
The Promise of Applying Machine Learning Techniques to Network Function Virtualization
Jmila, H., Khedher, M. I., & El‐Yacoubi, M. A. (2024) · International Journal of Network Dynamics and Intelligence · doi:10.53941/ijndi.2024.100020 · Cited by 5 · see citing works
Kim, H. (2022) · Springer eBooks · doi:10.1007/978-3-030-95041-5_10 · Cited by 2 · see citing works
A benchmarking of the efficiency of supervised ML algorithms in the NFV traffic classification
Reyes, J. A. V., Ordoñez, M. C. M., & Rendón, O. M. C. (2017) · Sistemas y Telemática · doi:10.18046/syt.v15i42.2539 · Cited by 2 · see citing works
Predicting Potentially Undetectable Flows in DPI System using Machine Learning
Saha, A., Trivedi, U., & Varun, S. (2021) · IEEE International Conference on Advanced Networks and Telecommunications Systems · doi:10.1109/ants52808.2021.9936960 · Cited by 2 · see citing works
Piakaray, D., Somashekhara Reddy, D., Goswami, S., Rajasekaran, S., Sharma, K., & Alfurhood, B. S. (2023) · International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE) · doi:10.1109/icacite57410.2023.10182596 · Cited by 2 · see citing works
QUIC SNI decryption usefulness prediction using Machine Learning
Kuppan, A. K., Bhat, R., & Trivedi, U. (2024) · IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT) · doi:10.1109/conecct62155.2024.10677263 · Cited by 1 · see citing works
Hierarchy Bayesian model based services awareness of high-speed optical access networks
Bai, H. (2018) · Optoelectronics Letters · doi:10.1007/s11801-018-7244-5 · Cited by 1 · see citing works
Evaluation of Machine Learning Algorithms for Automated Management of Wireless Links
Frias, F., Marcal, A. R. S., Prior, R., Moreira, W., & Oliveira-Jr, A. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-62362-3_2 · Cited by 1 · see citing works
IPv6 Flow-Label based Application Aware Routing in SDNs
Kushwaha, A. S., Bazard, N., & Gumaste, A. (2021) · IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS) · doi:10.1109/infocomwkshps51825.2021.9484442 · Cited by 1 · see citing works
Glisic, S., & Lorenzo, B. (2022) · Wiley eBooks · doi:10.1002/9781119790327.ch7
- Authors
- He, L., Xu, C., & Luo, Y.
- Affiliations
- University of Massachusetts Lowell, Lowell, MA, USA
- University of Massachusetts Lowell (US)
- ORCID
- Luo, Yan
- Venue
- Proceedings of the 2016 ACM International Workshop on Security in Software Defined Networks & Network Function Virtualization
- Publisher
- ACM
- Published
- 2016-03-11
- Pages
- 53-56
- OpenAlex
- W2323736524
- Cited by
- 31
- References
- 15
- Citations by year
- 2024: 2 · 2023: 3 · 2022: 3 · 2021: 3 · 2020: 6 · 2019: 6 · 2018: 3 · 2017: 4 · 2016: 1
- FWCI
- 3.99
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- ACM International Workshop on Security in Software Defined Networks & Network Function Virtualization (SDN-NFV Sec) (publishedVersion)
- Funders
- National Science Foundation (1547428 1541434 1440737 1450996)
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › Software-Defined Networks and 5G
- Physical Sciences › Computer Science › Artificial Intelligence › Internet Traffic Analysis and Secure E-voting
- Physical Sciences › Computer Science › Hardware and Architecture › Network Packet Processing and Optimization
- Keywords
- network flow classification, virtual network functions, network function virtualization, classifier selection, user privacy, flow characteristics, network security
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