Investigating DDOS attacks on metro network

A broad increase in data consumption in society and industry trigger network operators looking to upgrade their metro networks with higher bandwidth requirements. Service providers and operators are challenged to find a simple, the most efficient and cost-effective way of meeting the demand with new speeds and standards on the horizon. Distributed Denial of Service (DDoS) attack is a cyber-attack that uses a technique to flood the server, the system, or network of the targeted attack with unwanted traffic. The occurrence of DDoS attack on the metro networks can make the operating system unable to operate properly and even crash. DDoS can be prevented by monitoring traffic regularly, increasing server resource capacity and implementing multiple protection strategies. This paper investigates DDoS attacks by utilizing Information Gain feature Selection method based on metro network expert’s opinion. The main aim is to improve the detection accuracy as such may help the metro network optimally provides the necessary bandwidth. Then, Naïve Bayes and K-Nearest Neighbor (KNN) classifiers are considered for evaluating the selected features as basis for the attack detection. Experimental results using CICIDS-2018 dataset show that KNN outperforms Naïve Bayes classifier with the accuracy level of 99%.