Evaluation and Analysis: Internet of Things using Machine Learning Algorithms for Detection of DDoS Attacks

Abstract

Internet of Things (IoT) system is facing a large number of attacks nowadays. Distributed Denial of Ser-vices(DDoS) attack is the most reported attack in the field of security. Nowadays these attacks are increasing very rapidly. However, it has been difficult to predict and detect these attacks easily. In this paper, various existing machine learning (ML) algorithms are being analyzed that are used to predict and detect DDoS attacks using different existing datasets namely; ToN-IoT, CICDDoS2019 and BoT-IoT. The four types of different ML algorithms have been deployed which include K-Nearest Neighbour (KNN), Naive Bayes(NB), Decision Tree (DT) and Random Forest (RF). The major goal of this analysis is to choose the best ML technique. Comparing the existing BoT-IoT dataset to the other datasets utilised in this paper, provides the best measure. The evaluation findings demonstrate that the decision tree and random forest classifiers provide the highest levels of accuracy. The purpose of this paper is to discuss the security concerns associated with DDoS attacks and their mitigation.