EXAMINING THE IMPACT OF SECURITY MEASURES ON QUALITY OF SERVICE VARIATIONS IN IOT NETWORKS

Abstract

Abstract- the Internet of Things (IoT) and other new technologies have made it possible to connect devices all over the world to the internet. The main reason why these devices are often called "smart gadgets" is because they can send, receive, and process data. It is thought to be one of the fastest-growing technologies, and the number of people who use it keeps going up and up every day. The success of the Internet of Things depends on how much data is sent or received over the networks, how well the quality of service (QoS) is maintained, and how the energy limits of battery-powered devices are dealt with (IoT). At the network level, the parameters that are used to measure the quality of service are end-to-end delay, throughput, jitter, and packet delivery ratio. Since there are more Internet of Things devices connected to the network than ever before, it is very important to put a lot of focus on both the safety of the devices and the safety of the data being sent through the connections. In this paper, we tried to look at the algorithms that have been used to keep track of where source and sink nodes are so that they can't be broken into. We have also tried to figure out what effect these AODV protocols have on the quality of service that IoT networks offer. Researchers who study malware, people who work in the industry, and end users all know about them so that better ways to stop them can be used. Because of this, it is very important to make an accurate prediction of the malware early on to stop more damage. The goal of this research is to get rid of malware using K-Nearest Neighbors by predicting how it will act and then getting rid of it (KNN). We found that using these classifiers in the right way can make a big difference in how well predictions are made. MATLAB programme that was used to run the environment.