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[1] Shengli Du, Qiushuo Yan, Lijing Dong, JunfeiQiao, “Secure Consensus of Multiagent Systems With Input Saturation and Distributed Multiple DoS Attacks”, IEEE Transactions on Circuits and Systems II: Express Briefs, Volume: 69, Issue: 4, Year: 2022 [2] Hamza Chahed, Andreas J. Kassler, “Software-Defined Time Sensitive Networks Configuration and Management”, 2021 IEEE Conference on Network Function Virtualization and Software Defined Networks (NFV-SDN), Year: 2021 [3] Adel Alshamrani, “Reconnaissance Attack in SDN based Environments”, 2020 27th International Conference on Telecommunications (ICT), Year: 2020 [4] Nitin Varyani, Zhi-Li Zhang, David Dai, “QROUTE: An Efficient Quality of Service (QoS) Routing Scheme for Software-Defined Overlay Networks”, IEEE Access, Volume: 8, Year: 2020 [5] Jesús Arturo Pérez-Díaz, Ismael AmezcuaValdovinos, Kim-Kwang Raymond Choo, Dakai Zhu, “A Flexible SDN-Based Architecture for Identifying and Mitigating Low-Rate DDoS Attacks Using Machine Learning”, IEEE Access, Volume: 8, Year: 2020 [6] Abdullah H Almutairi, Nabih T Abdelmajeed, “Innovative signature based intrusion detection system: Parallel processing and minimized database”,2017 International Conference on the Frontiers and Advances in Data Science (FADS), Year: 2017 [7] MarwaneZekri, Said El Kafhali, Noureddine Aboutabit, Youssef Saadi, “DDoS attack detection using machine learning techniques in cloud computing environments”, 2017 3rd International Conference of Cloud Computing Technologies and Applications (CloudTech), Year: 2017 [8] Alshamrani, A. Chowdhary, A. Pisharody, S. Lu, D. Huang,“A defense system for defeating DDoS attacks in SDN based networks”, In Proceedings of the 15th ACM International Symposium on Mobility Management and Wireless Access, Miami Beach, FL, USA, 21–25 November 2017. [9] Z. Abaid, M. A. Kaafar, and S. Jha, “Quantifying the impact of adversarial evasion attacks on machine learning based android malware classifiers,” in 2017 IEEE 16th International Symposium on Network Computing and Applications (NCA), IEEE, 2017, pp. 1–10. [10] H. Lin and P. Wang, ‘‘Implementation of an SDN-based security defense mechanism against DDoS attacks,’’ in Proc. Joint Int. Conf. Econ. Manage. Eng. (ICEME), Int. Conf. Econ. Bus. Manage. (EBM), Philadelphia, PA, USA, 2016 [11] Barki, Lohit, “Detection of distributed denial of service attacks in software defined networks.” International Conference on Advances in Computing, Communications and Informatics (ICACCI), Sep 2016, pp: 2576-2581 [12] Nanda. S, Zafari. F, DeCusatis. C, Wedaa. E, Yang. B,“Predicting network attack patterns in SDN using machine learning approach”, In Proceedings of the 2016 IEEE Conference on Network Function Virtualization and Software Defined Networks (NFV-SDN), Palo Alto, CA, USA, 7–10 November 2016. [13] N. F. Haq, A. R. Onik, M. A. K. Hridoy, M. Rafni, F. M. Shah, and D.M. Farid, “Application of machine learning Approaches in Intrusion Detection system: A survey”, international journal of advanced Research in Artificial Intelligence, vol. 4, no. 3, pp. 9 18, 2015 [14] Gisung Kim, Seungmin Lee, Sehun Kim “A novel Hybrid attackDetection method integrating Anomaly Detection with misuse Detection”, - journal on Expert Systems with Applications, 41(4), March 2014, pp: 1690-1700 [15] B. Biggio, I. Corona, D. Maiorca, B. Nelson, N. Srndi c, P. Laskov, G. Giacinto, and F. Roli, “Evasion attacks against machine learning at test time”, in Joint European conference on machine learning and knowledge discovery in databases. Springer, 2013, pp. 387–402
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