Design of Deep Learning Models for the Identification of Harmful Attack Activities in the Industrial Internet of Things (IIOT)

Traditional industrial processes benefit greatly from the Industrial Internet of Things (IIoT), which increases efficiency as well as productivity. However, as IIoT becomes more connected, critical infrastructure is exposed to a variety of cyber risks. In order to accurately identify dangerous attack behaviors within IIoT systems, the present research focuses on the development and execution of deep learning models, combining recurrent neural networks (RNNs) including artificial neural networks (ANNs). The approach uses Python-based visualization techniques, model creation, and data pretreatment. By proactively identifying and mitigating cyber risks, our research seeks to strengthen IIoT security and ensure the resilience and honesty of industrial operations in a world that is becoming more linked.