Deep Learning-Enhanced Intrusion Detection and Privacy Preservation for IIoT Networks

Products that are more variable, have higher and more reliable quality, are cheaper, and have shorter life cycles are urgently needed right now on a worldwide scale. Scheduled production optimization, process coordination, minimizing asset interruptions cost reduction, and human error minimization are all goals of the Industrial Web, also called the Industrial Internet of Things (IIoT). This network is used by a wide range of businesses, including aerospace, automobile, farming, medical care, transport, energy, and more. Collecting, processing, analysing, and communicating massive amounts of data is the job of the IIoT networks' sophisticated sensors, sensors, equipment, embedded applications, and dispersed communication systems. Malicious actors can readily gain access to vulnerable devices in the IIoT and steal privacy-sensitive & security-critical monitoring information from IIoT networks. One critical component that has seen extensive usage in IIoT networks for the purpose of identifying malicious network activity is an intrusion detection system (IDS). IDS can identify malicious instances and disinfect the workplace. When compared to GA+SVM, CNN+LSTM, and Modified K-Nearest Neighbors (KNN), the suggested technique gets the best accuracy of 99.7 percent. Which means it can reliably and precisely distinguish between benign and malicious communications. The suggested approach pioneers a new standard in intrusion detection with its cutting-edge optimizations and deep learning approaches.