Detection and Mitigation of DDoS Attacks on SDN Controller in IoT Network using Gini Impurity

Abstract Nowadays, Distributed denial-of-service (DDoS) attacks are a serious threat to businesses and individuals, and it is getting more prevalent. Moreover, the exponential growth of IoT devices and their interdependency makes the technology more vulnerable to DDoS attacks. Therefore, this paper presents an efficient Gini-Impurity based method for detection and mitigation of DDoS attacks. The proposed approach uses the Gini impurity technique as a metric on the Software Defined Network (SDN) controller in IoT network to measure the homogeneity of the network traffic. Gini impurity based method is efficient, fast, and requires less computing power. The approach also uses a classifier to filter the network traffic. We evaluated the effectiveness of the proposed approach using real-world network traffic datasets. The detection rate of the proposed approach varies between 98% and 100%. We compared the proposed approach with existing methods, and it detects DDoS attacks early with high accuracy and a low false-positive rate.