7d6a8a50-8c8a-4284-9f9d-7fa906722cd7 beie director@blueeyesintelligence.org 10.35940/ijitee.C8356.0110321 10.1109/ACCESS.2018.2831284D. Yin, L. Zhang and K. Yang, "A DDoS Attack Detection and Mitigation With Software-Defined Internet of Things Framework," in IEEE Access, vol. 6, pp. 24694-24705, 2018, doi:10.1109/ACCESS.2018.2831284.10.1109/ACCESS.2018.2886601I. Ud Din, M. Guizani, S. Hassan, B.Kim, M. K. Khan, M. Atiquzzaman, and S. H. Ahmed, "The Internet of Things: A review of enabled technologies and future challenges," IEEE Access, vol. 7, pp. 7606-7640, 2018, 10.1109/ACCESS.2018.2886601.10.1109/ATNAC.2017.8215418S. S. Bhunia and M. Gurusamy, "Dynamic attack detection and mitigation in IoT using SDN," 2017 27th International Telecommunication Networks and Applications Conference (ITNAC), Melbourne, VIC, 2017, pp. 1-6, doi: 10.1109/ATNAC.2017.8215418.10.1109/ACCESS.2019.2920763X. Liu, C. Qian, W. G. Hatcher, H. Xu, W. Liao and W. Yu, "Secure Internet of Things (IoT)-Based Smart-World Critical Infrastructures: Survey, Case Study and Research Opportunities," in IEEE Access, vol. 7, pp. 79523-79544, 2019, doi: 10.1109/ACCESS.2019.2920763.10.1016/j.future.2019.08.007Z. A. Baig, S. Sanguanpong, S. N. Firdous, T. G. Nguyen, and C. So-In, "Averaged dependence estimators for DoS attack detection in IoT networks," Future Generation Computer Systems, vol. 102, pp. 198-209, 2020, 10.1016/j.future.2019.08.007.10.1109/CloudTech.2017.8284731M. Zekri, S. E. Kafhali, N. Aboutabit and Y. Saadi, "DDoS attack detection using machine learning techniques in cloud computing environments," 2017 3rd International Conference of Cloud Computing Technologies and Applications (CloudTech), Rabat, 2017, pp. 1-7, doi: 10.1109/CloudTech.2017.8284731.M. M. Salim, S. Rathore, and J. H. Park, "Distributed denial of service attacks and its defenses in IoT: a survey," The Journal of Supercomputing, pp. 1-44, 2019, 10.1007/s11227-019-02945-z.10.1109/ICCSP.2017.8286761V. Adat, and B. B. Gupta, "A DDoS attack mitigation framework for internet of things," in International conference on communication and signal processing (ICCSP), IEEE, pp. 2036-2041, 201710.1007/s00500-014-1250-8J. Choi, C. Choi, B. Ko, and P. Kim, "A method of DDoS attack detection using HTTP packet pattern and rule engine in cloud computing environment," Soft Computing, vol. 18, no. 9, pp. 1697-1703, 2014.10.1007/s41650-018-0022-5A. Lohachab, B. Karambir, "Critical Analysis of DDoS-An Emerging Security Threat over IoT Networks," Journal of Communications and Information Networks, vol. 3, no.3, 2018, 10.1007/s41650-018-0022-5.10.3390/e18100350K. J. Singh, K. Thongam, and T. De, "Entropy-based application layer DDoS attack detection using artificial neural networks," Entropy, vol. 18, no. 10, pp. 350, 2016.P. Redekar, and M. Chatterjee, "Hybrid technique for DDoS attack detection," International Journal of Computer Science and Information Technologies, vol. 8, no.3, pp. 377-379, 2017.10.23919/JCC.2019.07.012Z. Liu, Y. He, W. Wang, and B. Zhang, "DDoS attack detection scheme based on entropy and PSO-BP neural network in SDN," China Communications, vol. 16, no. 7, pp. 144-155, 2019.10.1109/NCA.2018.8548334S. Daneshgadeh, T. Kemmerich, T. Ahmed, and N. Baykal, "A Hybrid approach to detect DDoS attacks using KOAD and the mahalanobis distance," in IEEE 17th International Symposium on Network Computing and Applications (NCA), IEEE, pp. 1-5, 2018, 10.1109/NCA.2018.8548334.10.1016/j.jnca.2020.102756S. D. Çakmakçı, T. Kemmerich, T. Ahmed, and N. Baykal, "Online DDoS attack detection using Mahalanobis distance and Kernel-based learning algorithm," Journal of Network and Computer Applications, pp. 102756, 2020., 10.1016/j.jnca.2020.102756.10.1007/978-3-030-41568-6_24F. E. Ouerfelli, K. Barbaria, B. Zouari, and C. Fachkha, "Distributed detection system using wavelet decomposition and chi-square test," in International Conference on Risks and Security of Internet and Systems, Springer, Cham, pp. 365-377, 2019.10.1109/TEL-NET.2017.8343555G. S. Kushwah, and S. T. Ali, "Detecting DDoS attacks in cloud computing using ANN and black hole optimization," in 2nd International Conference on Telecommunication and Networks (TEL-NET), IEEE, pp. 1-5, 2017, 10.1109/TEL-NET.2017.8343555.10.1109/TSP.2016.7760838R. F. Fouladi, C. E. Kayatas, and E. Anarim, "Frequency based DDoS attack detection approach using naive Bayes classification," in 39th International Conference on Telecommunications and Signal Processing (TSP), IEEE, pp. 104-107, 2016, 10.1109/TSP.2016.7760838.10.1109/ICSCN.2017.8085703S. Lakshminarasimman, S. Ruswin and K. Sundarakantham, "Detecting DDoS attacks using decision tree algorithm," 2017 Fourth International Conference on Signal Processing, Communication and Networking (ICSCN), Chennai, 2017, pp. 1-6, doi: 10.1109/ICSCN.2017.8085703.10.1155/2018/9804061J. Ye, X. Cheng, J. Zhu, L. Feng, and L. Song, "A DDoS attack detection method based on SVM in software defined network," Security and Communication Networks, 2018, 10.1155/2018/9804061.10.4236/jis.2018.91004S. Bista, and R. Chitrakar, "DDoS attack detection using heuristics clustering algorithm and Naïve Bayes classification," Journal of Information Security, vol. 9, no. 01, pp. 33, 2017.10.1109/BIGCOMP.2017.7881684J. Kim, N. Shin, S. Y. Jo, and S. H. Kim, "Method of intrusion detection using deep neural network," in IEEE International Conference on Big Data and Smart Computing (BigComp), IEEE, pp. 313-316, 2017, 10.1109/BIGCOMP.2017.7881684.10.3390/app9020238Y. Yang, K. Zheng, C. Wu, X. Niu, and Y. Yang, "Building an effective intrusion detection system using the modified density peak clustering algorithm and deep belief networks," Applied Sciences, vol. 9, no. 2, pp. 238, 2019.10.1016/j.comnet.2019.107049Y. Zhong, W. Chen, Z. Wang, Y. Chen, K. Wang, Y. Li, X. Yin, X. Shi, J. Yang, and K. Li, "HELAD: A novel network anomaly detection model based on heterogeneous ensemble learning," Computer Networks, vol. 169, pp. 107049, 2020, 10.1016/j.comnet.2019.107049.10.1109/SPW.2018.00013R. Doshi, N. Apthorpe, and N. Feamster, "Machine learning ddos detection for consumer internet of things devices," in IEEE Security and Privacy Workshops (SPW), IEEE, pp. 29-35, 2018, 10.1109/SPW.2018.00013.10.1109/ACCESS.2019.2917532Y. Gu, K. Li, Z. Guo and Y. Wang, "Semi-Supervised K-Means DDoS Detection Method Using Hybrid Feature Selection Algorithm," in IEEE Access, vol. 7, pp. 64351-64365, 2019, doi: 10.1109/ACCESS.2019.2917532.10.1016/j.future.2020.03.049S. Velliangiri, , and H. M. Pandey, "Fuzzy-Taylor-elephant herd optimization inspired Deep Belief Network for DDoS attack detection and comparison with state-of-the-arts algorithms," Future Generation Computer Systems, 2020, 10.1016/j.future.2020.03.049.10.1016/j.cose.2019.101645M. Wang, Y. Lu, and J. Qin, "A dynamic MLP-based DDoS attack detection method using feature selection and feedback," Computers & Security, vol. 88, pp. 101645, 2020, 10.1016/j.cose.2019.101645.10.1007/s10586-018-2275-zC. Wang, H. Yao, and Z Liu, "An efficient DDoS detection based on SU-Genetic feature selection," Cluster Computing, vol. 22, no. 1, pp. 2505-2515, 2019.M. Aamir, and S. M. Zaidi, "Clustering based semi-supervised machine learning for DDoS attack classification," Journal of King Saud University-Computer and Information Sciences, 2019, 10.1016/j.jksuci.2019.02.003.