Machine Learning-Based Detection for Distributed Denial of Service Attack in IoT

Internet of Things is a popular source to collect data. It is also a rich source to various types of information. With the rapid popularity of IoT, things getting connected to it and the number are increasing continuously. Hence, the challenge associated with the proper maintenance of IoT networks in different sectors is increasing globally and the growing size is the ultimate reason for this problem. DDoS is one of the various attacks which are common and known. Botnets are being used to perform such attacks. Machine learning is a technology that has been supporting the standard computing environment in many ways. It can help design efficient models to identify attacks. Recent standard datasets and machine learning techniques, such as, Decision Trees, Random Forest, and KNN, are used in this work to ascertain DDoS attacks performed on IoT environments. These methods are compared by considering the confusion matrix created on the basis of different measures.