Detecting Distributed Denial of Service Attacks using Machine Learning Models
Ebtihal Sameer Alghoson, Onytra Abbass
Abstract
The Software Defined Networking (SDN) is a vital technology which includes decoupling the control and data planes in the network. The advantages of the separation of the control and data planes including: a dynamic, manageable, flexible, and powerful platform. In addition, a centralized network platform offers situations that challenge security, for instance the Distributed Denial of Service (DDoS) attack on the centralized controller. DDoS attack is a well-known malicious attack attempts to disrupt the normal traffic of targeted server, network, or service, by overwhelming the target’s infrastructure with a flood of Internet traffic. This paper involves investigating several machine learning models and employ them with the DDoS detection system. This paper investigates the issue of enhancing the DDoS attacks detection accuracy using a well-known DDoS named as CICDDoS2019 dataset. In addition, the DDoS dataset has been preprocessed using two main approaches to obtain the most relevant features. Four different machine learning models have been selected to work with the DDoS dataset. According to the results obtained from real experiments, the Random Forest machine learning model offered the best detection accuracy with (99.9974%), with an enhancement over the recent developed DDoS detection systems.