A Comprehensive Study on Denial of Service (DoS) Based on Feature Selection of a Given Set Datasets in Internet of Things (IoT)

Abstract

Internet of Things (IoT) has achieved great recognition, in terms of identifying datasets through feature selection to increase the performance of the IoT network. In this situation, attacks will play a crucial role in choosing the performance of IoT networks. The Existing methodology like labeled transition could able to collect the data in such a way that the data can be accessed using a classification mechanism but with less feature selection. This may not lead to dimensionality reduction which may lead to a larger number of feature selections and thus making the system complex. The current research papers will focus on dimensionality reduction with less feature selection and retrieve the maximal contents of the datasets. This would assist the IoT users with a machine learning model to retrieve the data with fewer threats on the system. This is due to the maximal selection of the traits. This may lead to maximal DoS and minimal datasets feature selection.