Analysis of Machine Learning Data Security in the Internet of Things (IoT) Circumstance

The current Internet of things (IoT) technologies will also have a profound economic, industrial and social influence on society. Teh ability of nodes to participate in IoT networks is generally resource-constrained, that also allows them to attract objectives for cyber threats. An extensive research has been conducted to address the issues of security and privacy in IoT networks, primarily through the conventional authentication approaches. The main goal is to review the current research learning related to the safety issues of the IoT and provides an information understanding on the topic. The manual scientific visualization study was carried out over a number of articles, and out of which, 58 research articles are selected for careful observation. From the articles, impacts and solutions with future research on security within IoT gateway have been identified. These visualization researchers have indicated key concerns as well as number of solutions. The findings also indicated the challenges in achieving secured data protection management and data center integration, which still require efficient solutions. The outcome of the physical institutional mapping study is complete with the use of automatic image analysis software on two datasets (–2016 and –2020) derived from the ML and IoT. This qualitative approach generates trends and over decades in particular to the IoT security. Researchers also discuss a number of future research directions for ML- and DL-based IoT security research.