Neutrosophic-based machine learning context for the trustworthiness of devices in the internet of things

Abstract

The industrial sector is among the most suited sectors that may considerably advantage from the implementation of the ideas and technology of the Industrial Internet of Things (IIoT), and it is one of the most competitive industries in the world. The increased use of automated processes in manufacturing sectors results in a wide variety of applications based on IIoT. These applications call for the efficient integration of a wide variety of different systems and the execution of smooth operations across all machines. The issue of integration and smooth operation presents IIoT as a new subject of study in smart manufacturing. This carries with it several problems, including those on security, accountability, confidence, and dependability. As part of the Industrial Internet of Things (IIoT), many devices will be linked to one another and interact with one another through wireless and internet infrastructure. When this kind of situation plays out, the reliability of the IIoT devices becomes a key component in the process of preventing injection by hostile machines. As a result, an intelligent computer model is required to effectively cluster and categorize the level of trustworthiness possessed by the IIoT devices. In this article, we describe a trust model for the Internet of Things (IIoT) that is based on the neutrosophic TOPSIS and is utilized by IIoT apps to determine the trust score of IIoT devices. The reliability of devices is evaluated by the model that was constructed using the historical knowledge, chronological knowledge, and network behavior information that is received from IIoT devices. In addition to that, the model suggests KNN, and a Decision tree to categorize the attributes that were collected.