Ensemble classification using traffic flow metrics to predict distributed denial of service scope in the Internet of Things (IoT) networks

Abstract

The IoT networks are highly vulnerable to distributed denial of service, which is the most serious and intolerable attack format. The competent solutions to handle DDOS attacks on IoT networks are limited. The contemporary research practices are more specific to incorporate machine learning to device novel defense measures to defend the DDOS attacks on IoT networks. This manuscript addressed high dimensionality in training data of the machine learning-based DDOS attack defense in IoT networks. It portrayed a novel ensemble classification using traffic flow metrics ECTFM as features to predict the DDOS attacks on IoT networks. Experimental results outcome of the cross-validation addressed in the experimental study addressing the importance of the proposed approach towards DDOS defense accuracy with less false alarming. The performance of the proposed approach has been scaled by comparing it with DDOS defense contemporary contributions in IoT networks.