Detection of DDoS attack in IoT traffic using ensemble machine learning techniques

A denial-of-service (DoS) attack aims to exhaust the resources of the victim by sending attack packets and ultimately stop the legitimate packets by various techniques. The paper discusses the consequences of distributed denial-of-service (DDoS) attacks in various application areas of Internet of Things (IoT). In this paper, we have analyzed the performance of machine learning(ML)-based classifiers including bagging and boosting techniques for the binary classification of attack traffic. For the analysis, we have used the benchmark CICDDoS2019 dataset which deals with DDoS attacks based on User Datagram Protocol (UDP) and Transmission Control Protocol (TCP) in order to study new kinds of attacks. Since these protocols are widely used for communication in IoT networks, this data has been used for studying DDoS attacks in the IoT domain. Since the data is highly unbalanced, class balancing is done using an ensemble sampling approach comprising random under-sampler and ADAptive SYNthetic (ADASYN) oversampling technique. Feature selection is achieved using two methods, i.e., (a) Pearson correlation coefficient and (b) Extra Tree classifier. Further, performance is evaluated for ML classifiers viz. Random Forest (RF), Nai & BULL;ve Bayes (NB), support vector machine (SVM), AdaBoost, eXtreme Gradient Boosting (XGBoost) and Gradient Boosting (GB) algorithms. It is found that RF has given the best performance with the least training and prediction time. Further, it is found that feature selection using extra trees classifier is more efficient as compared to the Pearson correlation coefficient method in terms of total time required in training and prediction for most classifiers. It is found that RF has given best performance with least time along with feature selection using Pearson correlation coefficient in attack detection.