A Machine Learning-based Real-time Monitoring System for Classification of Elephant Flows on KOREN |
Akbar, Waleed
(Department of Computer Engineering, Jeju National University)
Rivera, Javier J.D. (Department of Electrical Engineering, Jeju National University) Ahmed, Khan T. (Department of Computer Engineering, Jeju National University) Muhammad, Afaq (Department of Computer Engineering, Jeju National University) Song, Wang-Cheol (Department of Computer Engineering, Jeju National University) |
1 | Jang, RhongHo, et al., "Rflow+: An sdn-based wlan monitoring and management framework," in Proc. of IEEE INFOCOM 2017-IEEE Conference on Computer Communications, IEEE, 2017. |
2 | Suarez-Varela, Jose, and Pere Barlet-Ros, "Sbar: Sdn flow-based monitoring and application recognition," in Proc. of the Symposium on SDN Research, pp. 1-2, 2018. |
3 | Ajaeiya, Georgi A., et al., "Flow-based intrusion detection system for SDN," in Proc. of 2017 IEEE Symposium on Computers and Communications (ISCC), IEEE, 2017. |
4 | https://scikit-learn.org/stable/modules/grid_search.html |
5 | Afaq, Muhammad, Shafqat Ur Rehman, and Wang-Cheol Song, "Visualization of elephant flows and qos provisioning in sdn-based networks," in Proc. of 2015 17th Asia-Pacific Network Operations and Management Symposium (APNOMS), IEEE, 2015. |
6 | Gomes, Heitor Murilo, et al., "A survey on ensemble learning for data stream classification," ACM Computing Surveys (CSUR), 50(2), 1-36, 2018. DOI |
7 | Amin, Rashid, Martin Reisslein, and Nadir Shah, "Hybrid SDN networks: A survey of existing approaches," IEEE Communications Surveys & Tutorials, 20(4), 3259-3306, 2018. DOI |
8 | Oh, Chaeyeon, Joonseo Ha, and Heejun Roh, "A Survey on TLS-Encrypted Malware Network Traffic Analysis Applicable to Security Operations Centers," Applied Sciences, 12(1), 155, 2022. |
9 | Zhao, Jingjing, et al., "Network traffic classification for data fusion: A survey," Information Fusion, 72, 22-47, 2021. DOI |
10 | Fu, Yinlin, et al., "Gaussian mixture model with feature selection: An embedded approach," Computers & Industrial Engineering, 152, 107000, 2021. |
11 | https://grafana.com/ |
12 | Afaq, Muhammad, Shafqat Rehman, and Wang-Cheol Song, "Large flows detection, marking, and mitigation based on netFlow standard in SDN," Journal of Korea Multimedia Society, 18(2), 189-198, 2015. DOI |
13 | Berde, Pankaj, et al., "ONOS: towards an open, distributed SDN OS," in Proc. of the third workshop on Hot topics in software defined networking, pp. 1-6, 2014. |
14 | https://www.koren.kr/kor/index.asp |
15 | https://prometheus.io/ |
16 | https://prometheus.io/docs/guides/node-exporter/ |
17 | Manning, Christopher, Prabhakar Raghavan, and Hinrich Schutze, "Introduction to information retrieval," Natural Language Engineering, 16(1), 100-103, 2010. DOI |
18 | https://github.com/prometheus/pushgateway |
19 | Nugraha, Muhammad, et al., "Utilizing OpenFlow and netFlow to detect and mitigate SYN flooding attack," Journal of Korea Multimedia Society, 17(8), 988-994, 2014. DOI |
20 | https://prometheus.io/docs/instrumenting/exporters/ |
21 | Lee, Changhun, and Chiehyeon Lim, "From technological development to social advance: A review of Industry 4.0 through machine learning," Technological Forecasting and Social Change, 167, 120653, 2021. |
22 | Chen, Yuxin, et al., "Spectral methods for data science: A statistical perspective," Foundations and Trends® in Machine Learning, 14(5), 566-806, 2021. DOI |
23 | Cavalcante Araujo Neto, Antonio, "A Framework for Hierarchical Density-Based Clustering Exploration," 2021. |
24 | https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.kendalltau.html |
25 | https://scikit-learn.org/stable/modules/generated/sklearn.decomposition.PCA.html |
26 | David Paper, "Introduction to scikit-learn," Hands-on Scikit-Learn for Machine Learning Applications: Data Science Fundamentals with Python, pp. 1-35, 2020. |
27 | Shu, Jun Hua, Jiang Jiang, and Jing Xuan Sun, "Network traffic classification based on deep learning," Journal of Physics: Conference Series, Vol. 1087. No. 6, 2018. DOI |
28 | Greff, Klaus, Sjoerd Van Steenkiste, and Jurgen Schmidhuber, "Neural expectation maximization," arXiv preprint arXiv:1708.03498, 2017. |
29 | Isolani, Pedro Heleno, et al., "Interactive monitoring, visualization, and configuration of OpenFlow-based SDN," in Proc. of 2015 IFIP/IEEE International Symposium on Integrated Network Management (IM), IEEE, 2015. |
30 | Queiroz, Wander, Miriam AM Capretz, and Mario Dantas, "An approach for SDN traffic monitoring based on big data techniques," Journal of Network and Computer Applications, 131, 28-39, 2019. DOI |
31 | Cheng, Tracy Yingying, and Xiaohua Jia, "Compressive traffic monitoring in hybrid SDN," IEEE Journal on Selected Areas in Communications, 36(12), 2731-2743, 2018. DOI |
32 | Kambar, Mina Esmail Zadeh Nojoo, et al., "A survey on mobile malware detection methods using machine learning," in Proc. of 2022 IEEE 12th Annual Computing and Communication Workshop and Conference (CCWC), IEEE, 2022. |
33 | Makarychev, Konstantin, and Liren Shan, "Near-optimal algorithms for explainable k-medians and k-means," in Proc. of International Conference on Machine Learning, PMLR, 2021. |
34 | Bogineni, K., et al., "SDN-NFV reference architecture," Verizon, New York City, NY, USA, Verizon Network Infrastructure Planning Version 1, 2016. |
35 | Van Tu, Nguyen, Jonghwan Hyun, and James Won-Ki Hong, "Towards onos-based sdn monitoring using in-band network telemetry," in Proc. of 2017 19th Asia-Pacific Network Operations and Management Symposium (APNOMS), IEEE, 2017. |
![]() |