Detection of Distributed Denial of Service (DDOS) Attack Using Logistic Regression and K Nearest Neighbor Algorithms - Priya, G. Gnana; Shriram, S. Harini; Jeeva, S.; Priya, G. Sakthi; Balasubadra, K. (International Journal of Intelligent Systems and Applications in Engineering, 2024)
SDN (Software Defined Network) devices are controlled in a centralized manner and it is better when compared to all other traditional networks. Some advantages of SDN such as greater scalability, high programmability, security features and management. In SDN, DDOs attack occurs certainly. Attacks such as DDOS (Distributed Denial of Service) pose a foremost risk in maintaining the security of the network and it also shut down the network fully. Traditional techniques do not work as well to identify the DDOS attack. Hence, in order to identify the DDOS attack, we employ certain machine learning algorithms. In our work, we compare two algorithms of Machine Learning (ML) such as Logistic Regression (LR) and K-Nearest Neighbors (KNN) and the accuracy is also compared. The accuracy of the two algorithms differs in our experimental results. The accuracy of Logistic Regression is roughly 91% and the accuracy of the KNN algorithm is roughly 99%. From the analysis KNN is better rather than Logistic Regression.