3105fa3d-4d6d-439d-b3a9-9ffd02721ceb20220228085615891naun:naunmdt@crossref.orgMDT DepositInternational Journal of Circuits, Systems and Signal Processing1998-446410.46300/9106http://www.naun.org/cms.action?id=30291720221720221610.46300/9106.2022.16https://npublications.com/journals/circuitssystemssignal/2022.phpVanyaIvanovaPhD School, French Faculty of Electrical Engineering Technical University of Sofia 8 Kliment Ohridski Blvd., 1756 Sofia BulgariaTashoTashevPhD School, French Faculty of Electrical Engineering Technical University of Sofia 8 Kliment Ohridski Blvd., 1756 Sofia BulgariaIvoDraganovPhD School, French Faculty of Electrical Engineering Technical University of Sofia 8 Kliment Ohridski Blvd., 1756 Sofia BulgariaIn this paper following the recent trends in IoT-based network attacks discovery and advancing further our previous research, in which we optimize and test single neural network, support vector machine and random forest classifiers for both the detection and recognition of multiple DDoS attacks, we propose results from newly developed combined classifiers. The first of them employs only a neural network and a random forest classifier, while the second use additionally a support vector machine. Both are implemented in two modifications – as detectors of malicious vs. normal traffic, and as classifiers of 10 types of attacks vs. non-attack samples. High classification accuracy is being obtained over the popular Bot-IoT dataset and it prove higher than that of the single classifiers. At the same time, it is also higher than other solutions, proposed in the practice.2282022228202275476393https://npublications.com/journals/circuitssystemssignal/2022/b882005-093(2022).pdf10.46300/9106.2022.16.93https://npublications.com/journals/circuitssystemssignal/2022/b882005-093(2022).pdf10.1007/s11192-020-03819-5Hamid, H., Noor, R. M., Omar, S. N., Ahmedy, I., Anjum, S. S., Shah, S. A. A., Kaur, S., Othman, F., Tamil, E. M., IoT-based Botnet Attacks Systematic Mapping Study of Literature. Scientometrics, Vol. 126, No. 4, 2021, pp. 2759-2800. 10.1109/icoin.2018.8343104Koay, A., Chen, A., Welch, I., Seah, W. K., A New Multi Classifier System using Entropy-based Features in DDoS Attack Detection. In 2018 International Conference on Information Networking (ICOIN), IEEE, January 2018, pp. 162-167. 10.1109/qrs-c.2019.00090Das, S., Mahfouz, A. M., Venugopal, D., Shiva, S., DDoS Intrusion Detection through Machine Learning Ensemble. In 2019 IEEE 19th International Conference on Software Quality, Reliability and Security Companion (QRS-C), IEEE, July 2019, pp. 471-477. 10.1109/icc40277.2020.9149043Musumeci, F., Ionata, V., Paolucci, F., Cugini, F., Tornatore, M. Machine-learning-assisted DDoS Attack Detection with P4 Language. In ICC 2020-2020 IEEE International Conference on Communications (ICC), IEEE, June 2020, pp. 1-6. 10.3390/fi12110180Mahfouz, A., Abuhussein, A., Venugopal, D., Shiva, S., Ensemble Classifiers for Network Intrusion Detection Using a Novel Network Attack Dataset. Future Internet, Vol. 12, No. 11, 2020, Art. No. 180. 10.11591/ijece.v10i3.pp2543-2550Algelal, Z. M., Aldhaher, E. A. G., Abdul-Wadood, D. N., Al-Sagheer, R. H. A., Botnet Detection using Ensemble Classifiers of Network Flow. International Journal of Electrical and Computer Engineering, Vol. 10, No. 3, 2020, pp. 2543-2550. 10.3390/electronics8111210Khraisat, A., Gondal, I., Vamplew, P., Kamruzzaman, J., Alazab, A., A Novel Ensemble of Hybrid Intrusion Detection System for Detecting Internet of Things Attacks, Electronics, Vol. 8, No. 11, 2019, Art. No. 1210. 10.1155/2020/4586875Rajagopal, S., Kundapur, P. P., Hareesha, K. S., A Stacking Ensemble for Network Intrusion Detection using Heterogeneous Datasets. Security and Communication Networks, Vol. 2020, Art. No. 4586875, 2020. 10.3390/s20092559Iwendi, C., Khan, S., Anajemba, J. H., Mittal, M., Alenezi, M., Alazab, M., The Use of Ensemble Models for Multiple Class and Binary Class Classification for Improving Intrusion Detection Systems, Sensors, Vol. 20, No. 9, 2020, Art. No. 2559. Jain, A. K., Dhawan, H., Sowmiya, B., DDoS Detection Using Machine Learning Ensemble. Turkish Journal of Computer and Mathematics Education (TURCOMAT), Vol. 12, No. 12, 2021, pp. 1647-1655. 10.1016/j.comnet.2020.107247Zhou, Y., Cheng, G., Jiang, S., Dai, M., Building an Efficient Intrusion Detection System based on Feature Selection and Ensemble Classifier. Computer Networks, Vol. 174, 2020, Art. No. 107247. 10.46300/9106.2022.16.81Ivanova, V., T. Tashev, I. Draganov, Detection of IoT based DDoS Attacks by Network Traffic Analysis using Feedforward Neural Networks, WSEAS, 2021 (under review). 10.46300/9106.2022.16.84Ivanova, V., Multiple IoT based Network Attacks Discrimination by Multilayer Feedforward Neural Networks, WSEAS, 2021 (under review). 10.37394/23209.2022.19.1Ivanova, V., T. Tashev, I. Draganov, DDoS Attacks Classification using SVM, WSEAS, 2021 (under review). Ivanova, V., T. Tashev, I. Draganov, Random Forest Detector and Classifier of Multiple IoT-based DDoS Attacks, WSEAS, 2021 (under review). 10.1016/j.future.2019.05.041Koroniotis, N., Moustafa, N., Sitnikova, E., Turnbull, B., Towards the Development of Realistic Botnet Dataset in the Internet of Things for Network Forensic Analytics: Bot-IoT dataset. Future Generation Computer Systems, Vol. 100, November 2019, pp. 779-796. Hosmer Jr, D. W., Lemeshow, S., Sturdivant, R. X., Applied Logistic Regression, 3 ed., John Wiley & Sons, 2013. Kidambi, R., Stochastic Gradient Descent for Modern Machine Learning: Theory, Algorithms and Applications, PhD Thesis, University of Washington, 2019. Campisi, P., Egiazarian, K., Blind Image Deconvolution: Theory and Applications, CRC Press, 2007. Pavlov, Y. L., Random Forests, De Gruyter, 2019. Steinwart, I., Christmann, A., Support Vector Machines, Springer, 2008. (Journal Online Sources style) K. Author. (year, month). Title. Journal [Type of medium]. Volume(issue), paging if given. Available: http://www.(URL) 10.1109/27.57528R. J. Vidmar. (1992, August). On the use of atmospheric plasmas as electromagnetic reflectors. IEEE Trans. Plasma Sci. [Online]. 21(3). pp. 876—880. Available: http://www.halcyon.com/pub/journals/21ps03-vidmar