Design and Implementation of Efficient Counter-Based IoT DDoS Attacks Detection System Using Machine Learning

Abstract

AbstractThe traditional infrastructures are assisted by introducing the promising applications of Internet of things (IoT) (smart cities, smart homes, smart girds and smart health) with smart objects. In cloud servers, DDoS attacks happened and cause a problem of overwhelming. But Internet of things (IoT) devices increase in number which leads to cause the large-scale DDoS attacks influence from the IoT devices. Therefore, design and implementation of efficient counter-based IoT DDoS attack detection system using machine learning is proposed in this paper. Different network parameters values are used in detection of abnormal defense activities and DDoS attacks by the proposed framework. With the help of wired or wireless networks, the required dataset as sensor data is collected from the different sensors which are equipped on the eight smart poles which are constructed on certain campus. According to types of DDoS attacks, the features are extracted. Different machine learning classifiers are used in this proposed DDoS attack detection method as neural network, LSVM, random tree and decision tree (J-48). In the real IoT environment, DDoS attack detection method with best accuracy is obtained by using feature selection. Therefore, from the experimental results, the accuracy performance is having or achieving the higher accuracy. IoT DDoS attacks detection system results effectively block the harmed devices.KeywordsDDoS attacksMachine learning (ML)Internet of things (IoT)Software defined networks (SDN)