EDP Sciences logo
Web of Conferences logo
Search
Menu
All issues Volume 65 (2024) ITM Web Conf., 65 (2024) 04002 References
Open Access
Issue
ITM Web Conf.
Volume 65, 2024
International Conference on Multidisciplinary Approach in Engineering, Technology and Management for Sustainable Development: A Roadmap for Viksit Bharat @ 2047 (ICMAETM-24)
Article Number 04002
Number of page(s) 10
Section Electricals and Electronics Engineering
DOI https://doi.org/10.1051/itmconf/20246504002
Published online 16 July 2024
  1. Cisco Visual Networking Index: Global Mobile Data Traffic Forecast Update, 2017-2023 WhitePaper; Cisco: San Jose, CA, USA. [Google Scholar]
  2. Ayoubi, S.; Limam, N.; Salahuddin, M.A.; Shahriar, N.; Boutaba, R.; Estrada-Solano, F.; Caicedo, O.M. Machine learning for cognitive network management. IEEE Commun. Mag. 2018, 56, 158–165. [CrossRef] [Google Scholar]
  3. Mestres, A.; Rodriguez-Natal, A.; Carner, J.; Barlet-Ros, P.; Alarcón, E.; Solé, M.; Muntés-Mulero, V.; Meyer, D.; Barkai, S.; Hibbett, M.J.; et al. Knowledge-defined networking. ACM SIGCOMM Comput. Commun. Rev. 2017, 47, 2–10. [CrossRef] [Google Scholar]
  4. Xie, J.; Yu, F.R.; Huang, T.; Xie, R.; Liu, J.; Wang, C.; Liu, Y. A survey of machine learning techniques applied to software defined networking (SDN): Research issues and challenges. IEEE Commun. Surv. Tutor. 2018, 21, 393–430. [Google Scholar]
  5. Latah, M.; Toker, L. Application of Artificial Intelligence to Software Defined Networking: A survey. Indian J. Sci. Technol. 2016, 9, 1–7. [CrossRef] [Google Scholar]
  6. Latah, M.; Toker, L. Artificial Intelligence enabled Software-Defined Networking: A comprehensive overview. IET Netw. 2018, 8, 79–99. [Google Scholar]
  7. Zhao, Y.; Li, Y.; Zhang, X.; Geng, G.; Zhang, W.; Sun, Y. A survey of networking applications applying the Software Defined Networking concept based on machine learning. IEEE Access 2019, 7, 95397–95417. [CrossRef] [Google Scholar]
  8. Thupae, R.; Isong, B.; Gasela, N.; Abu-Mahfouz, A.M. Machine learning techniques for traffic identification and classification in SDWSN: A survey. In Proceedings of the 44th Annual Conference of the IEEE Industrial Electronics Society, Washington, DC, USA, 21-23 October 2018; pp. 4645–4650. [Google Scholar]
  9. Mohammed, A.R.; Mohammed, S.A.; Shirmohammadi, S. Machine Learning and Deep Learning based traffic classification and prediction in Software Defined Networking. In Proceedings of the IEEE International Symposium on Measurements & Networking (M&N), Catania, Italy, 8-10 July 2019; [Google Scholar]
  10. Boutaba, R.; Salahuddin, M.A.; Limam, N.; Ayoubi, S.; Shahriar, N.; Estrada-Solano, F.; Caicedo, O.M. A comprehensive survey on machine learning for networking: Evolution, applications and research opportunities. J. Internet Serv. Appl. 2018, 9, 1–99. [CrossRef] [Google Scholar]
  11. Sultana, N.; Chilamkurti, N.; Peng, W.; Alhadad, R. Survey on SDN based network intrusion detection system using machine learning approaches. PeerNetw. Appl. 2019, 12, 493–501. [Google Scholar]
  12. Nguyen, T.N. The challenges in SDN/ML based network security: A survey. arXiv 2018, arXiv:1804.03539. [Google Scholar]
  13. Pouyanfar, S.; Sadiq, S.; Yan, Y.; Tian, H.; Tao, Y.; Reyes, M.P.; Shyu, M.L.; Chen, S.C.; Iyengar, S. A survey on deep learning: Algorithms, techniques, and applications. ACM Comput. Surv. (CSUR) 2018, 51, 1–36. [Google Scholar]
  14. Deng, L. A tutorial survey of architectures, algorithms, and applications for deep learning. APSIPA Trans. Signal Inf. Process. 2014, 3, e2. [Google Scholar]
  15. Khalid, S.; Khalil, T.; Nasreen, S. A survey of feature selection and feature extraction techniques in machine learning. In Proceedings of the Science and Information Conference, London, UK, 27-29 August 2014; pp. 372–378. [Google Scholar]
  16. Liu, H.; Yu, L. Toward Integrating Feature Selection Algorithms for Classification and Clustering. IEEETrans. Knowl. DataEng. 2005, 491-502. [Google Scholar]
  17. Kobo, H.I.; Abu-Mahfouz, A.M.; Hancke, G.P. A survey on Software-Defined Wireless Sensor Networks: Challenges and design requirements. IEEE Access 2017, 5, 1872–1899. [CrossRef] [Google Scholar]
  18. Domingos, P. A few useful things to know about machine learning. Commun. ACM 2012, 55, 78–87. [CrossRef] [Google Scholar]
  19. Jordan, M.I.; Mitchell, T.M. Machine learning: Trends, perspectives, and prospects. Science 2015, 349, 255–260. [CrossRef] [Google Scholar]
  20. Mitchell, T.M. Machine Learning; McGraw-Hill: New York, NY, USA, 1997. [Google Scholar]
  21. Bengio, Y.; Lee, H. Editorial introduction to the neural networks special issue on deep learning of representations. Neural Netw. 2015, 64, 1–3. [CrossRef] [Google Scholar]
  22. Pedregosa, F.; Varoquaux, G.; Gramfort, A.; Michel, V.; Thirion, B.; Grisel, O.; Blondel, M.; Prettenhofer, P.; Weiss, R.; Dubourg, V.; et al. Scikit-learn: Machine learning in Python. J. Mach. Learn. Res. 2011, 12, 2825–2830. [Google Scholar]
  23. Zhang, G.P. Neural networks for classification: A survey. IEEE Trans. Syst. Man, Cybern. 2000, 30, 451–462. [CrossRef] [Google Scholar]
  24. Pacheco, F.; Exposito, E.; Gineste, M.; Baudoin, C.; Aguilar, J. Towards the deployment of machine learning solutions in network traffic classification: A systematic survey. IEEE Commun. Surv. Tutor. 2019, 21, 1988–2014. [CrossRef] [Google Scholar]
  25. Aouedi, O.; Piamrat, K.; Parrein, B. Performance evaluation of feature selection and tree-based algorithms for traffic classification. In Proceedings of the 2021 IEEE International Conference on Communications Workshops (ICC Workshops), Montreal, QC, Canada 14-23 June 2021. [Google Scholar]
  26. Tomar, D.; Agarwal, S. A survey on Data Mining approaches for Healthcare. Int. J. Bio-Sci. Bio-Technol. 2013, 5, 241–266. [CrossRef] [Google Scholar]
  27. Zhu, X.J. Semi-SupervisedLearningLiteratureSurvey; TechnicalReport; UniversityofWisconsin-MadisonDepartmentofComputer Sciences: Madison, WI, USA, 2005. [Google Scholar]
  28. Aouedi, O.; Piamrat, K.; Bagadthey, D. A semi-supervised stacked autoencoder approach for network traffic classification. In Proceedings of the 2020 IEEE 28th International Conference on Network Protocols (ICNP); Madrid, Spain, 13-16 October 2020. [Google Scholar]
  29. Sutton, R.S.; Barto, A.G.; others. Introduction to Reinforcement Learning, MIT Press: Cambridge, UK, 1998; Volume 135. [Google Scholar]
  30. Watkins, C.J.; Dayan, P. Q-learning. Machine Learn. 1992, 8, 279–292. [Google Scholar]
  31. Boyan, J.A.; Littman, M.L. Packet routing in dynamically changing networks: A reinforcement learning approach. In Advances in Neural Information Processing Systems. Available online: https://proceedings.neurips.cc/paper/1993/hash/4ea06fbc83cdd0a06020c35d50e1e89aAbstract.html (accessed on 30 December 2021). [Google Scholar]
  32. Bitaillou, A.; Parrein, B.; Andrieux, G. Q-routing: From the algorithm to the routing protocol. In Proceedings of the International Conference on Machine Learning for Networking, Paris, France, 3-5 December 2019; pp. 58–69. [Google Scholar]
  33. Mnih, V.; Kavukcuoglu, K.; Silver, D.; Graves, A.; Antonoglou, I.; Wierstra, D.; Riedmiller, M. Playing atari with deep reinforcement learning. arXiv 2013, arXiv:1312.5602. [Google Scholar]
  34. Li, Y. Deep reinforcement learning: An overview. arXiv 2017, arXiv:1701.07274. [Google Scholar]
  35. Ketkar, N.; Santana, E. Deep Learning with Python; Springer: Berlin/Heidelberg, Germany, 2017; Volume 1. [CrossRef] [Google Scholar]
  36. Zhang, Q.; Yang, L.T.; Chen, Z.; Li, P. A survey on deep learning for big data. Inf. Fusion 2018, 42, 146–157. [CrossRef] [MathSciNet] [Google Scholar]
  37. Bengio, Y.; Courville, A.; Vincent, P. Representation learning: A review and new perspectives. IEEE Trans. Pattern Anal. Mach. Intell. 2013, 35, 1798–1828. [CrossRef] [PubMed] [CrossRef] [Google Scholar]
  38. Mayer, R.; Jacobsen, H.A. Scalable deep learning on distributed infrastructures: Challenges, techniques, and tools. ACM Comput. Surv. (CSUR) 2020, 53, 1–37. [Google Scholar]
  39. Shrestha, A.; Mahmood, A. Review of deep learning algorithms and architectures. IEEE Access 2019, 7, 53040–53065. [CrossRef] [Google Scholar]
  40. Krizhevsky, A.; Sutskever, I.; Hinton, G.E. Imagenet classification with deep convolutional neural networks. Adv. Neural Inf. Process. Syst. 2012, 25, 1097–1105. [Google Scholar]
  41. Haykin, S. Neural Networks: A Comprehensive Foundation; Prentice-Hall: Hoboken, NJ, USA, 2007. [Google Scholar]
  42. Woz´niak, M.; Grana, M.; Corchado, E. A survey of multiple classifier systems as hybrid systems. Inf. Fusion 2014, 16, 3–17. [CrossRef] [Google Scholar]
  43. Li, Y.; Pan, Y. A novel ensemble deep learning model for stock prediction based on stock prices and news. Int. J. Data Sci. Anal. 2021, 1–11. [Google Scholar]
  44. Polikar, R. Ensemble based systems in decision making. IEEE Circuits Syst. Mag. 2006, 6, 21–45. [CrossRef] [Google Scholar]
  45. Tang, T.A.; Mhamdi, L.; McLernon, D.; Zaidi, S.A.R.; Ghogho, M. Deep learning approach for network intrusion detection in Software Defined Networking. In Proceedings of the 2016 International Conference on Wireless Networks and Mobile Communications (WINCOM), Fez, Morocco, 26-29 October 2016; pp. 258–263. [Google Scholar]
  46. Freund, Y.; Mason, L. The alternating decision tree learning algorithm. In Proceedings of the 16th International Conference on Machine Learning (ICML), Bled, Slovenia, 27-30 June 1999; Volume 99, pp. 124–133. [Google Scholar]
  47. Breiman, L. Random forests. Mach. Learn. 2001, 45, 5–32. [CrossRef] [Google Scholar]
  48. Abar, T.; Letaifa, A.B.; El Asmi, S. Machine learning based QoE prediction in SDN networks. In Proceedings of the 13th International Wireless Communications and Mobile Computing Conference (IWCMC), Valencia, Spain, 26-30 June 2017; pp. 1395–1400. [Google Scholar]
  49. Schapire, R.E. The boosting approach to machine learning: An overview. In Nonlinear Estimation and Classification; Springer: New York, NY, USA, 2003; pp. 149–171. [CrossRef] [Google Scholar]
  50. Dainotti, A.; Pescape, A.; Claffy, K.C. Issues and future directions in traffic classification. IEEENetw. 2012, 26, 35–40. [Google Scholar]
  51. L’heureux, A.; Grolinger, K.; Elyamany, H.F.; Capretz, M.A. Machine learning with big data: Challenges and approaches. IEEE Access 2017, 5, 7776–7797. [CrossRef] [Google Scholar]
  52. Janecek, A.; Gansterer, W.; Demel, M.; Ecker, G. On the relationship between feature selection and classification accuracy. In Proceedings of the New Challenges for Feature Selection in Data Mining and Knowledge Discovery, Antwerp, Belgium, 15 September 2008; Volume 4, pp. 90–105. [Google Scholar]
  53. Chu, C.T.; Kim, S.K.; Lin, Y.A.; Yu, Y.; Bradski, G.; Olukotun, K.; Ng, A.Y. Map-reduce for machine learning on multicore. Adv. Neural Inf. Process. Syst. 2007, 19, 281–288. [Google Scholar]
  54. Guyon, I.; Weston, J.; Barnhill, S.; Vapnik, V. Gene selection for cancer classification using support vector machines. Mach. Learn. 2002, 46, 389–422. [CrossRef] [Google Scholar]
  55. Motoda, H.; Liu, H. Feature selection, extraction and construction. Commun. IICM (Institute Inf. Comput. Mach. Taiwan) 2002, 5, 2. [Google Scholar]
  56. Rangarajan, L.; others. Bi-level dimensionality reduction methods using feature selection and feature extraction. Int. J. Comput. Appl. 2010, 4, 33–38. [Google Scholar]
  57. Pal, M.; Foody, G.M. Feature selection for classification of hyperspectral data by SVM. IEEE Trans. Geosci. Remote Sens. 2010, 48, 2297–2307. [CrossRef] [Google Scholar]
  58. Stadler, R.; Pasquini, R.; Fodor, V. Learning from network device statistics. J. Netw. Syst. Manag. 2017, 25, 672–698. [CrossRef] [CrossRef] [Google Scholar]
  59. Da Silva, A.S.; Machado, C.C.; Bisol, R.V.; Granville, L.Z.; Schaeffer-Filho, A. Identification and selection of flow features for accurate traffic classification in SDN. In Proceedings of the 14th International Symposium on Network Computing and Applications, Cambridge, MA, USA, 28-30 September 2015; pp. 134–141. [Google Scholar]
  60. Xiao, P.; Qu, W.; Qi, H.; Xu, Y.; Li, Z. An efficient elephant flow detection with cost-sensitive in SDN. In Proceedings of the 1st International Conference on Industrial Networks and Intelligent Systems (INISCom), Tokyo, Japan, 2-4 March 2015; pp. 24–28. [Google Scholar]
  61. Wang, P.; Lin, S.C.; Luo, M. A framework for QoS-aware traffic classification using semi-supervised machine learning in SDNs. In Proceedings of the IEEE International Conference on Services Computing (SCC), San Francisco, CA, USA, 27 June–2 July 2016; pp. 760–765. [Google Scholar]
  62. Amaral, P.; Dinis, J.; Pinto, P.; Bernardo, L.; Tavares, J.; Mamede, H.S. Machine learning in software defined networks: Data collection and traffic classification. In Proceedings of the 24th International Conference on Network Protocols (ICNP), Singapore, 8-11 November 2016; pp. 1–5. [Google Scholar]
  63. Zhang, C.; Wang, X.; Li, F.; He, Q.; Huang, M. Deep learning-based network application classification for SDN. Trans. Emerg. Telecommun. Technol. 2018, 29, e3302. [CrossRef] [Google Scholar]
  64. Hongyan He, Guoyan Huang, Bing Zhang, Zhangqi Zheng, “Research on DoS Traffic Detection Model Based on Random Forest and Multilayer Perceptron”, Security and Communication Networks, vol. 2022, Article ID 2076987, 11 pages, 2022. https://doi.org/10.1155/2022/2076987 [Google Scholar]
  65. D. Tang, S. Zhang, J. Chen, and X. Wang, “The detection of low-rate DoS attacks using the SADBSCAN algorithm,” Information Sciences, vol. 565, pp. 229–247, 2021. [CrossRef] [MathSciNet] [Google Scholar]
  66. J. S. M. Osorio, J. A. V. Tejada, and J. F. B. Vega, “Detection of DoS/DDoS attacks: the UBM and GMM approach,” in Proceedings of the 2021 IFIP/IEEE International Symposium on Integrated Network Management (IM), pp. 866–871, IEEE, France, May 2021. [Google Scholar]
  67. S. S. T. Reddy and G. K. Shyam, “An efficient metaheuristic algorithm based feature selection and recurrent neural network for DoS attack detection in cloud computing environment,” Applied Soft Computing, vol. 100, Article ID 106997, 2021. [Google Scholar]
  68. J. A. Perez-Diaz, I. A. Valdovinos, and K. K. R. Choo, “A flexible SDN-based architecture for identifying and mitigating low-rate DDoS attacks using machine learning,” IEEE Access, vol. 8, no. 99, pp. 155859–155872, 2020. [CrossRef] [Google Scholar]
  69. D. Kshirsagar and S. Kumar, “An efficient feature reduction method for the detection of DoS attack,” ICT Express, vol. 7, no. 3, pp. 371–375, 2021. [CrossRef] [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.