DDoS Attacks Detection and Mitigation in SDN Using Machine Learning

Submitted by grigby1 on Wed, 02/26/2020 - 4:42pm

Title	DDoS Attacks Detection and Mitigation in SDN Using Machine Learning
Publication Type	Conference Paper
Year of Publication	2019
Authors	Rahman, Obaid, Quraishi, Mohammad Ali Gauhar, Lung, Chung-Horng
Conference Name	2019 IEEE World Congress on Services (SERVICES)
Date Published	jul
Publisher	IEEE
ISBN Number	978-1-7281-3851-0
Keywords	Computer crime, computer network security, control systems, DDoS, DDoS attack detection, distributed denial-of-service attack, Floods, J48, k-nearest neighbors, machine learning, Measurement, Metrics, nearest neighbour methods, Network topology, privacy, pubcrawl, Random Forest, random forests, SDN, SDN network, security threat protection, software defined networking, support vector machine, Support vector machines, threat vectors, WEKA
Abstract	Software Defined Networking (SDN) is very popular due to the benefits it provides such as scalability, flexibility, monitoring, and ease of innovation. However, it needs to be properly protected from security threats. One major attack that plagues the SDN network is the distributed denial-of-service (DDoS) attack. There are several approaches to prevent the DDoS attack in an SDN network. We have evaluated a few machine learning techniques, i.e., J48, Random Forest (RF), Support Vector Machine (SVM), and K-Nearest Neighbors (K-NN), to detect and block the DDoS attack in an SDN network. The evaluation process involved training and selecting the best model for the proposed network and applying it in a mitigation and prevention script to detect and mitigate attacks. The results showed that J48 performs better than the other evaluated algorithms, especially in terms of training and testing time.
URL	https://ieeexplore.ieee.org/document/8817237
DOI	10.1109/SERVICES.2019.00051
Citation Key	rahman_ddos_2019

Groups:

Science of Security VO