Ko, K.-M.; Baek, J.-M.; Seo, B.-S.; Lee, W.-B. Comparative Study of AI-Enabled DDoS Detection Technologies in SDN. Appl. Sci.2023, 13, 9488.
Ko, K.-M.; Baek, J.-M.; Seo, B.-S.; Lee, W.-B. Comparative Study of AI-Enabled DDoS Detection Technologies in SDN. Appl. Sci. 2023, 13, 9488.
Ko, K.-M.; Baek, J.-M.; Seo, B.-S.; Lee, W.-B. Comparative Study of AI-Enabled DDoS Detection Technologies in SDN. Appl. Sci.2023, 13, 9488.
Ko, K.-M.; Baek, J.-M.; Seo, B.-S.; Lee, W.-B. Comparative Study of AI-Enabled DDoS Detection Technologies in SDN. Appl. Sci. 2023, 13, 9488.
Abstract
Software Defined Networking (SDN) is positioning the standard for the management of networks due to its scalability and flexibility to program the network. The SDN provides many advantages but it also involves some specific security problems take down the controller using cyber attack and in result the whole network will shut down which makes it a single point of failure. In this paper, the DDoS attacks in SDN were detected using AI-enabled, machine and deep learning, models with some specific features for data-set under normal and DDoS traffic. In our approach, initial data-set is collected from 84 features on kaggle and then the 20 top most features are selected using permutation importance algorithm. The data-set were learned and tested with AI-enabled 5 models. Our experimental results showed that the use of machine learning based random forest model has achieved the highest accuracy rate of 99.97%, in DDoS attack detection in SDN. Our contributions through this study are, first, we found highest 20 attacks that absolutely contributed to DDoS attacks. Secondly, it can reduce the time and cost of comparing various learning models and performance required for determining a learning model suitable for DDoS detection. Finally, various experimental methods for evaluating the performance of the learning model are presented so that related researchers can utilize them.
Keywords
SDN, DDoS attacks; Deep learning; Machine learning; Permutation Importance
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
