Anomalies Detection on Contemporary Industrial Internet of Things Data for Securing Crucial Devices

Abstract

The industrial Internet of things (IIoT) is a framework that enables companies to improve their processes and increase their efficiency by using smart sensors and actuators. It is also used to collect data from various industrial devices and make use of this data to improve their operations. With the help of connected sensors and actuators, companies can save time. Due to the increasing number of industrial Internet of things (IIoT) devices, the data generated by these networks is constantly being analyzed. However, due to the high dimensionality of the data, it can cause fragmentation. Also, datasets collected by IIoT nodes are prone to displaying anomalous events. Unlike traditional networks, which are usually composed of several applications and protocols, IIoT networks are different. They require more stringent security measures to protect the integrity and confidentiality of their data. Due to the unique features of IIoT networks, they are prone to attack. This paper briefly describes the various requirements and challenges faced when it comes to protecting these networks. It also presents evaluation of various machine learning classifiers to detect anomalous behaviors in these networks. Due to the nature of the data that these networks contain anomalies, their analysis is critical to prevent attacks. In this paper, we analyze the usage of various machine learning algorithms to check the efficiency and accuracy of detecting anomalies in data generated by crucial IIoT devices.