Optimal Attacks Classification in Edge Internet of Things Networks Using Deep Learning Algorithm

As a result of the proliferation of applications for the Internet of Things (IoT), people's day-to-day lives have become less complicated. This has concurrently led to an increase in the productivity of both individuals and organizations. The vulnerability of these technologies to attacks is a direct result of the numerous security breaches that have taken place in large-scale information technology systems. These kinds of attacks provide a substantial risk to the user right to privacy as well as the sensitive information that they keep within their systems. As a result of this, it is necessary to have a trustworthy intrusion system to enhance the detection capabilities of EIoT networks. We are developing a classifier that makes use of dense neural networks (DenseNets) to classify input data that is derived from the datasets that are currently accessible as part of this area of research. The classification phases encompass a variety of different sorts of methods, such as pre-processing and feature extraction. The purpose of these steps is to enhance the categorizing process. Using a copy of an environment that comprises Internet of Things devices, we might determine whether DenseNets are effective in threat classification. For example, when compared to alternative deep learning architectures that are currently available, the results of the simulation demonstrate that the technique that was suggested resulted in a significant improvement in classification rate.