A Machine Learning-Based Intrusion Detection of DDoS Attack on IoT Devices

DDoS attack aims to prevent legitimate users from getting access to a targeted system service by exhausting the resources, bandwidth and so on. Though, there are different intrusion mechanisms for detection DDoS attack, having an automated system that can learn the nature of the attack and instantly detect it is the reason why machine learning is used in this work. Decision tree, KNN and Nave Bayes are the algorithms used classify a benign traffic from a DDoS attack. About nineteen different feature was carefully selected from CIC2019DDoS dataset. The DDoS attack types used for the experiment are UDP, DNS, SYN and NetBIOS. The results of the experiment indicate that Decision tree and KNN proved to be the most effective with an accuracy of 100% and 98% respectively. Nave Bayes gave a very poor result with an accuracy of 29%.