Self-Supervised Learning Approaches for Traffic Engineering in Software-Defined Networks
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Downstream tasks can be accomplished by fine-tuning or by using a linear classifier.
“Down streaming can be accomplished in two ways: fine-tuning or using a linear classifier [25].”
From Self-supervised learning: A succinct review (2023) · cites this paper as [25]
Cites
Moustafa, N., & Slay, J. (2015) · 2015 Military Communications and Information Systems Conference (MilCIS) · doi:10.1109/milcis.2015.7348942 · Cited by 4,033 · see citing works
Internet traffic classification using bayesian analysis techniques
Moore, A. W., & Zuev, D. V. (2005) · ACM SIGMETRICS International Conference on Measurement and Modeling of Computer Systems (SIGMETRICS) · doi:10.1145/1064212.1064220 · Cited by 1,216 · see citing works
Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications
Alsheikh, M. A., Lin, S., Niyato, D. T., et al. (2014) · Singapore Management University Institutional Knowledge (InK) (Singapore Management University) · doi:10.48550/arxiv.1405.4463 · Cited by 581 · see citing works
A roadmap for traffic engineering in SDN-OpenFlow networks
Akyildiz, I. F., Lee, A., Wang, P., Luo, M., & Chou, W. (2014) · Computer Networks · doi:10.1016/j.comnet.2014.06.002 · Cited by 536 · see citing works
A Survey of Payload-Based Traffic Classification Approaches
Finsterbusch, M., Richter, C., Rocha, E., Muller, J., & Hanssgen, K. (2014) · IEEE Communications Surveys and Tutorials · doi:10.1109/surv.2013.100613.00161 · Cited by 310 · see citing works
Provisioning IP backbone networks to support latency sensitive traffic
Fraleigh, C., Tobagi, F. A., & Diot, C. (2004) · doi:10.1109/infcom.2003.1208689 · Cited by 171 · see citing works
Artificial intelligence enabled software‐defined networking: a comprehensive overview
Latah, M., & Toker, L. (2018) · IET Networks · doi:10.1049/iet-net.2018.5082 · Cited by 162 · see citing works
Traffic Engineering in SDN/OSPF Hybrid Network
Guo, Y., Wang, Z., Yin, X., Shi, X., & Wu, J. (2014) · IEEE International Conference on Network Protocols (ICNP) · doi:10.1109/icnp.2014.90 · Cited by 137 · see citing works
Congestion-aware single link failure recovery in hybrid SDN networks
Chu, C.-Y., Xi, K., Luo, M., & Chao, H. J. (2015) · IEEE Conference on Computer Communications (INFOCOM) · doi:10.1109/infocom.2015.7218482 · Cited by 122 · see citing works
Decision Trees and Random Forests
Fratello, M., & Tagliaferri, R. (2017) · Elsevier eBooks · doi:10.1016/b978-0-12-809633-8.20337-3 · Cited by 118 · see citing works
Network Traffic Classification using Machine Learning Techniques over Software Defined Networks
Parsaei, M. R., Javad, M., Raouf, S., et al. (2017) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2017.080729 · Cited by 76 · see citing works
LBBSRT: An efficient SDN load balancing scheme based on server response time
Zhong, H., Fang, Y., & Cui, J. (2016) · Future Generation Computer Systems · doi:10.1016/j.future.2016.10.001 · Cited by 68 · see citing works
Survey on Software-Defined Networking
Chen, J., Zheng, X., & Rong, C. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-28430-9_9 · Cited by 65 · see citing works
Load balancing for multiple traffic matrices using SDN hybrid routing
Zhang, J., Xi, K., Luo, M., & Chao, H. J. (2014) · IEEE International Conference on High Performance Switching and Routing (HPSR) · doi:10.1109/hpsr.2014.6900880 · Cited by 59 · see citing works
Identification and Selection of Flow Features for Accurate Traffic Classification in SDN
da Silva, A. S., Machado, C. C., Bisol, R. V., et al. (2015) · IEEE International Symposium on Network Computing and Applications (NCA) · doi:10.1109/nca.2015.12 · Cited by 59 · see citing works
Detecting heavy flows in the SDN match and action model
Afek, Y., Bremler-Barr, A., Feibish, S. L., et al. (2018) · Computer Networks · doi:10.1016/j.comnet.2018.02.018 · Cited by 58 · see citing works
Elephant Flow Detection and Load-Balanced Routing with Efficient Sampling and Classification
Tang, F., Zhang, H., Yang, L. T., & Chen, L. (2019) · IEEE Transactions on Cloud Computing · doi:10.1109/tcc.2019.2901669 · Cited by 55 · see citing works
Dynamic load balancing in SDN-based data center networks
Zakia, U., & Yedder, H. B. (2017) · IEEE Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON) · doi:10.1109/iemcon.2017.8117206 · Cited by 49 · see citing works
Traffic engineering with Equal-Cost-Multipath: An algorithmic perspective
Chiesa, M., Kindler, G., & Schapira, M. (2014) · doi:10.1109/infocom.2014.6848095 · Cited by 45 · see citing works
Two phase load balanced routing using OSPF
Antić, M., Maksić, N., Knezevic, P., et al. (2010) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2010.100106 · Cited by 42 · see citing works
BeaQoS: Load balancing and deadline management of queues in an OpenFlow SDN switch
Boero, L., Cello, M., Garibotto, C., et al. (2016) · Computer Networks · doi:10.1016/j.comnet.2016.06.025 · Cited by 39 · see citing works
A Survey of Traffic Classification in Software Defined Networks
Yan, J., & Yuan, J. (2018) · International Conference on Hot Information-Centric Networking (HotICN) · doi:10.1109/hoticn.2018.8606038 · Cited by 39 · see citing works
Freeway: Adaptively Isolating the Elephant and Mice Flows on Different Transmission Paths
Wang, W., Sun, Y., Zheng, K., et al. (2014) · Proceedings/Proceedings - International Conference on Network Protocols · doi:10.1109/icnp.2014.59 · Cited by 37 · see citing works
Joint QoS and Congestion Control Based on Traffic Prediction in SDN
Tajiki, M. M., Akbari, B., Shojafar, M., et al. (2017) · Applied Sciences · doi:10.3390/app7121265 · Cited by 25 · see citing works
DYNAMIC TRAFFIC PATTERN CLASSIFICATION USING ARTIFICIAL NEURAL NETWORKS
Hua, J., & Faghri, A. (1993) · Transportation Research Record Journal of the Transportation Research Board · Cited by 21 · see citing works
Network Traffic Classification Using Machine Learning Algorithms
Shafiq, M. F., Yu, X., & Wang, D. (2017) · Advances in intelligent systems and computing · doi:10.1007/978-3-319-69096-4_87 · Cited by 20 · see citing works
Multi-feature Enterprise Traffic Characterization in OpenFlow-based Software Defined Networks
Bakhshi, T. (2017) · International Conference on Frontiers of Information Technology (FIT) · doi:10.1109/fit.2017.00012 · Cited by 10 · see citing works
Fast Packet Classification Algorithms
Dixit, M., Kale, A., Narote, M., et al. (2012) · International Journal of Computer Theory and Engineering · doi:10.7763/ijcte.2012.v4.632 · Cited by 8 · see citing works
Using Burstiness for Network Applications Classification
Oudah, H., Ghita, B., Bakhshi, T., Alruban, A., & Walker, D. J. (2019) · Journal of Computer Networks and Communications · doi:10.1155/2019/5758437 · Cited by 8 · see citing works
WOSPF: A Traffic Engineering Solution for OSPF Networks
Mishra, A., Sahoo, A., Dalvi, B. B., et al. (2016) · Globecom · doi:10.1109/glocom.2016.7842393 · Cited by 5 · see citing works
Parameter Estimation for Heuristic-based Internet Traffic Classification
Finsterbusch, M., Richter, C., & Müller, J.-A. (2012) · Cited by 4 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Self-supervised learning: A succinct review
Rani, V., Nabi, S. T., Kumar, M., Mittal, A., & Kumar, K. (2023) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-023-09884-2 · Cited by 295 · see citing works
- Authors
- Isravel, D. P., Silas, S., & Rajsingh, E. B.
- Affiliations
- Karunya University (IN)
- Venue
- Advances in Intelligent Systems and Computing
- Publisher
- Springer Singapore
- Published
- 2021
- Pages
- 511-522
- ISSN
- 2194-5357 (ISSN-L)
- 2194-5365
- OpenAlex
- W3168522608
- Cited by
- 1
- References
- 31
- Citations by year
- 2023: 1
- FWCI
- 0.23
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Advances in intelligent systems and computing (publishedVersion)
- 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 › Computer Networks and Communications › Network Security and Intrusion Detection
- Keywords
- software-defined networking, traffic engineering, self-supervised learning, network traffic classification, anomaly detection, traffic scheduling, quality of service, network congestion, packet loss, SDN controller
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
