[1] I. Cviti´c, D. Perakovi´c, B. Gupta, K. K. R. Choo, Boosting-based DDoS detection in the

internet of things systems. IEEE Int. Things J., 2021.

[2] Albulayhi K.; Smadi, A.A. Sheldon, F.T. Abercrombie, R.K, ―IoT Intrusion Detection

Taxonomy, Reference Architecture, and Analyses. Sensors 12, 6432, 2021.

[3] Statistical Portal. Internet of Things (IoT) Connected Devices Installed Base Worldwide from

2015 to 2025 (in Billions). Available online: https://www.statista.com/statistics/471264/iotnumber-

of-connected-devices-worldwide.

[4] Rose, K.; Eldridge, S.; Chapin, L. The Internet of Things: An Overview Understanding the

Issues and Challenges of a More Connected World. 2015.

[5] Cisco, Cisco Visual Networking Index (VNI) global Mobile data traffic Forecast update, 2017–

2022, Cisco Systems Inc., San Jose, CA, USA, 2019.

[6] Broadcom, ―Symantec Internet Security Threat Report 2019. 24, 2020.

[7] A. Marzano, D. Alexander, O. Fonseca et al., ―The Evolution of Bashlite and Mirai IoT botnets.

Proceedings of the IEEE Symposium on Computers and Communications, 813–818, IEEE,

Natal, Brazil, 2018.

[8] Mohit kumar, ―IoT botnets found using Default Credentials for C&C server Databases. 2020,

[9] Bankinfosecurity, ―Massive botnet attack used more than 400,000 IoT devices. 2020,

https://www.bankinfosecurity.com/massivebotnet-attack-used-more-than-400000-iotdevices-a-

12841.

[10] Enigmasoftware, ―BASHLITE Malware Hits Over One Million IoT Devices. 2020,

https://www.enigmasoftware.com/ bashlite-malware-hits-one-million-iot-devices/.

[11] Thingbots,―The Future of Botnets in the Internet of Things. 2020,

https://securityintelligence.com/thingbots-the-futureof- botnets-in-the-internet-of-things.

[12] S. A. R. Shah and B. Issac, ―Performance comparison of intrusion detection systems and

application of machine learning to Snort system .Future Generation Computer Systems, 80,

157–170, 2018.

[13] M. A. Ferrag and L. D.C. Maglaras, ―A novel deep learning and Blockchain-based Energy

Exchange framework for smart Grids. IEEE Transactions on Engineering Management, 67)4),

2019.

[14] M. A. Ferrag, L. Maglaras, S. Moschoyiannis, and H. Janicke, ―Deep learning for cyber security

intrusion detection: approaches, datasets, and comparative study. Journal of Information Security

and Applications, 50,102419, 2020.

[15] O. Alkadi, N. Moustafa, B. Turnbull, and K. K. R. Choo, ―A deep Blockchain frameworkenabled

Collaborative intrusion detection for protecting IoT and Cloud networks. IEEE Internet

Things J, 8(12), 2020.

[16] M. A. Al-Garadi, A. Mohamed, A. Al-Ali, X. Du, I. Ali, and M. Guizani, ―A Survey of Machine

and Deep Learning Methods for Internet of Things (IoT) Security. IEEE Communications

Surveys & Tutorials, 22(3), 2018.

[17] X. Xie, D. Wu, S. Liu, and R. Li, ―IoT Data Analytics Using Deep Learning,‖ 2017,

https://arxiv.org/abs/1708.03854.

[18] F. Alam, R. Mehmood, I. Katib, and A. Albeshri, ―Analysis of eight data mining algorithms for

smarter internet of things (IoT). Procedia Computer Science, 98, 437–442, 2016.

[19] X. Li, P. Yi, W. Wei, Y. Jiang, Tian, and L. Lnnls-Kh, ―A feature selection method for network

intrusion detection. Secur. Commun. Netw., Article ID 8830431, 22 pages, 2021.

[20] S. Yilmaz and S. Sen, ―Early detection of botnet Activities using Grammatical Evolution,‖ in

Applications of Evolutionary Computation., 395–404, Springer International Publishing,

Berlin/Heidelberg, Germany, 2019.

[21] M. Mazini, B. Shirazi, and I. Mahdavi, ―Anomaly network based intrusion detection system

using a reliable hybrid artificial bee colony and AdaBoost algorithms. Journal of King Saud

University - Computer and Information Sciences, 31(4), 541–553, 2019.

[22] A. Al Shorman, H. Faris, and I. Aljarah, ―Unsupervised intelligent system based on one class

support vector machine and Grey Wolf optimization for IoT botnet detection. Journal of

Ambient Intelligence and Humanized Computing, 11 (7), 2809–2825, 2020.

[23] K.-C. Lin, S.-Y. Chen, and J. C. Hung, ―Botnet detection using support vector machines with

artificial fish Swarm algorithm. Journal of Applied Mathematics, pp. 1–9, 2014.

[24] Y. Yu, J. Long, F. Liu, and Z. Cai, ―Machine learning combining with visualization for intrusion

detection: a survey. Proceedings of the International Conference on Modeling Decisions for

Artificial Intelligence, 239–249, Springer, Cham, Sant Juli`a de L`oria, Andorra, September

2016.

[25] K. Shinan, K. Alsubhi, A. Alzahrani, and M. U. Ashraf, ―Machine learning-based botnet

detection in software-defined network: a systematic review. Symmetry, 13 (5), 2021.

[26] M. Alauthman, N. Aslam, M. Al-kasassbeh, S. Khan, A. Al- Qerem, and K.-K. Raymond Choo,

―An efficient reinforcement learning-based Botnet detection approach. Journal of Network and

Computer Applications, 150, Article ID 102479, 2020.

[27] Abu Al-Haija, Q. Top-Down Machine Learning-Based Architecture for Cyberattacks

Identification and Classification in Io Communication Networks. Front. Big Data 2022.

[28] Abu Al-Haija, Q.; Al-Badawi, A. Attack-Aware IoT Network Traffic Routing Leveraging

Ensemble Learning. Sensors, 22, 241, 2022.

[29] Al-Haija, Q.A.; Saleh, E.; Alnabhan, M. Detecting Port Scan Attacks Using Logistic Regression.

Proceedings of the 2021 4th International Symposium on Advanced Electrical and

Communication Technologies (ISAECT), Khobar, Saudi Arabia, 1-5, 2021.

[30] Tsogbaatar, E.; Bhuyan, M.H.; Taenaka, Y.; Fall, D.; Gonchigsumlaa, K.; Elmroth, E.;

Kadobayashi, Y. DeL-IoT: A deep ensemble learning approach to uncover anomalies in IoT.

Internet Things 2021.

[31] Rezaei, A. Using Ensemble Learning Technique for Detecting Botnet on IoT. SN Comput. Sci.

4, 2021.

[32] Tsogbaatar, E.; Bhuyan, M.H.; Taenaka, Y.; Fall, D.; Gonchigsumlaa, K.; Elmroth, E.;

Kadobayashi, Y. DeL-IoT: A deep ensemble learning approach to uncover anomalies in IoT.

Internet Things 2021, 14.

[33] Rezaei, A. Using Ensemble Learning Technique for Detecting Botnet on IoT. SN Comput. Sci.

4, 2021.

[34] Özçelik, M.; Chalabianloo, N.; Gür, G. Software-Defined Edge Defense against IoT-Based

DDoS. Proceedings of the IEEE International Conference on Computer and Information

Technology (CIT 17), Helsinki, Finland, 21–23 August 2017.

[35] Summerville, D.H.; Zach, K.M.; Chen, Y. Ultra-Lightweight Deep Packet Anomaly Detection

for Internet of Things Devices. Proceedings of the 2015 IEEE 34th International Performance

Computing and Communications Conference (IPCCC 15), Mamkomg, China, 14–16 December

2015.

[36] Yang, L.; Shami, A. A Lightweight Concept Drift Detection and Adaptation Framework for IoT

Data Streams. IEEE Internet Things Mag. , 4, 96-101, 2021.

[37] Qaddoura, R.; Al-Zoubi, A.M.; Almomani, I.; Faris, H. A Multi-Stage Classification Approach

for IoT Intrusion Detection Based on Clustering with Oversampling. Appl. Sci. 11, 2021.

[38] Shi, W.C.; Sun, H.M. DeepBot: A time-based botnet detection with deep learning. Soft.

Comput. 24, 16605-16616, 2020.

[39] Nguyen, H.-T.; Ngo, Q.-D.; Le, V.-H. IoT Botnet Detection Approach Based on PSI graph and

DGCNN classifier. Proceedings of the 2018 IEEE International Conference on Information

Communication and Signal Processing (ICICSP), Singapore, 118-122, September 2018.

[40] McDermott, C.D.; Majdani, F.; Petrovski, A.V. Botnet Detection in the Internet of Things using

Deep Learning Approaches. In Proceedings of the 2018 International Joint Conference on Neural

Networks (IJCNN), Rio de Janeiro, Brazil, 1-8, 2018.

[41] Stiawan, D.; Suryani, M.E.; Susanto; Idris, M.Y.; Aldalaien, M.N.; Alsharif, N.; Budiarto, R.

Ping Flood Attack Pattern Recognition Using a K-Means Algorithm in an Internet of Things

(IoT) Network. IEEE Access , 9, 116475–116484, 2021.

[42] S. Khorsandroo, A. G. Sánchez, A. S. Tosun, J. M. Arco, and R. Doriguzzi-Corin, "Hybrid SDN

evolution: A comprehensive survey of the state-of-the-art. Computer Networks, 192, 107981,

2021.

[43] M. Jammal, T. Singh, A. Shami, R. Asal, and Y. Li, "Software defined networking: State of the

art and research challenges. Computer Networks, 72, 74-98, 2014.

[44] A. Bonguet and M. Bellaiche, "A survey of denial-of-service and distributed denial of service

attacks and defenses in cloud computing. Future Internet, 9, 43, 2017.

[45] B. Chu, T. J. Holt, and G. J. Ahn, "Examining the creation, distribution, and function of

malware on-line," National Institute of Justice, Washington, DC, 2010.

[46] E. C. Ogu, O. A. Ojesanmi, O. Awodele, and S. Kuyoro, "A botnets circumspection: The

current threat landscape, and what we know so far. Information, 10, 337, 2019.

[47] T. Mahjabin, Y. Xiao, G. Sun, and W. Jiang, "A survey of distributed denial-of-service attack,

prevention, and mitigation techniques. International Journal of Distributed Sensor Networks, 13,

1550147717741463, 2017.

[48] I. Sreeram and V. P. K. Vuppala, "HTTP flood attack detection in application layer using

machine learning metrics and bio inspired bat algorithm. Applied computing and informatics, 15,

59-66, 2019.

[49] D. Kreutz, F. M. Ramos, P. E. Verissimo, C. E. Rothenberg, S. Azodolmolky, and S. Uhlig,

"Software-defined networking: A comprehensive survey," Proceedings of the IEEE, 103, 14-76,

2014.

[50] N. Sultana, N. Chilamkurti, W. Peng, and R. Alhadad, "Survey on SDN based network intrusion

detection system using machine learning approaches," Peer-to-Peer Networking and

Applications, vol. 12, pp. 493-501, 2019.

[51] B. Isyaku, M. S. Mohd Zahid, M. Bte Kamat, K. Abu Bakar, and F. A. Ghaleb, "Software

defined networking flow table management of openflow switches performance and security

challenges: A survey," Future Internet, vol. 12, p. 147, 2020.

[52] S. M. Mousavi and M. St-Hilaire, "Early detection of DDoS attacks against SDN controllers.

Proceeding of international conference on computing, networking and communications (ICNC),

77-81, 2015.

[53] K. Shinan, K. Alsubhi, A. Alzahrani, and M. U. Ashraf, "Machine learning-based botnet

detection in software-defined network: a systematic review. Symmetry, 13, 866, 2021.

[54] T. Abhiroop, S. Babu, and B. Manoj, "A machine learning approach for detecting DoS attacks

in SDN switches," Proceeding of National Conference on Communications (NCC), 1-6. 2018.

[55] J. 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.

[56] R. Santos, D. Souza, W. Santo, A. Ribeiro, and E. Moreno, "Machine learning algorithms to

detect DDoS attacks in SDN. Concurrency and Computation: Practice and Experience, 32,

e5402, 2020.

[57] M. S. Elsayed, N.-A. Le-Khac, S. Dev, and A. D. Jurcut, "Machine-learning techniques for

detecting attacks in SDN," in 2019 IEEE 7th International Conference on Computer Science and

Network Technology (ICCSNT), 277-281, 2019.

[58] M. Wang, Y. Lu, and J. Qin, "A dynamic MLP-based DDoS attack detection method using

feature selection and feedback. Computers & Security, 88, 101645, 2020.

[59] B. Karan, D. Narayan, and P. Hiremath, "Detection of DDoS attacks in software defined

networks," Proceeding of International Conference on Computational Systems and Information

Technology for Sustainable Solutions (CSITSS), 265-270, 2018.

[60] Y. Liu, M. Dong, K. Ota, J. Li, and J. Wu, "Deep reinforcement learning based smart mitigation

of DDoS flooding in software-defined networks," Proceeding of IEEE International Workshop

on Computer Aided Modeling and Design of Communication Links and Networks (CAMAD),

1-6, 2018.

[61] C. Li, Y. Wu, X. Yuan, Z. Sun, W. Wang, X. Li, et al., "Detection and defense of DDoS attack–

based on deep learning in OpenFlow‐based SDN. International Journal of Communication

Systems, 31, e3497, 2018.

[62] A. Jose, L. R. Nair, and V. Paul, "Mitigation of Distributed Denial of Service (DDoS) Attacks

over Software Defined Networks (SDN) using Machine Learning and Deep Learning

Techniques. International Journal of Innovative Technology and Exploring Engineering

(IJITEE), 8, 2019.

[63] S. Haider, A. Akhunzada, I. Mustafa, T. B. Patel, A. Fernandez, K.-K. R. Choo, et al., "A deep

CNN ensemble framework for efficient DDoS attack detection in software defined networks.

IEEE Access, 8, 53972-53983, 2020.

[64] El-Kenawy, El-Sayed M., Marwa Eid, and Alshimaa H. Ismail. "A New Model for Measuring

Customer Utility Trust in Online Auctions." International Journal of Computer Applications 975:

8887.

[65] El-kenawy, El-Sayed M., Hattan F. Abutarboush, Ali Wagdy Mohamed, and Abdelhameed

Ibrahim. "Advance artificial intelligence technique for designing double T-shaped monopole

antenna." CMC-COMPUTERS MATERIALS & CONTINUA 69, no. 3 (2021): 2983-2995.

[66] El-kenawy, El-Sayed M., Marwa M. Eid, and Abdelhameed Ibrahim. "Anemia estimation for

covid-19 patients using a machine learning model." Journal of Computer Science and

Information Systems 17, no. 11 (2021): 2535-1451.

[67] Ibrahim, Abdelhameed, Seyedali Mirjalili, Mohammed El-Said, Sherif SM Ghoneim, Mosleh M.

Al-Harthi, Tarek F. Ibrahim, and El-Sayed M. El-Kenawy. "Wind speed ensemble forecasting

based on deep learning using adaptive dynamic optimization algorithm." IEEE Access 9 (2021):

125787-125804.

[68] Mohamed Saber, Efficient Phase Recovery System. Indonesian Journal of Electrical Engineering

and Computer Science, 5 (1), 123-129, 2017.

[69] Mohamed Saber, A novel design and Implementation of FBMC transceiver for low power

applications. Indonesian Journal of Electrical Engineering and Informatics, 8(1), 83-93, 2020.