Deep Learning Based Early Intrusion Detection in IIoT using Honeypot

The increasing number of Industrial Internet of Things (IIoT) devices presents hackers with a huge attack surface from which to conduct possibly more destructive assaults. Numerous of these assaults were successful as a consequence of the hackers' inventive and unique approaches. Due to the unpredictability of network technology and attack attempts, traditional Deep Learning (DL) approaches are made ineffective. The accuracy of DL algorithms has been shown across a range of scientific fields. The Convolutional Neural Network Model (CNN) technique is an ideal alternative for anomaly detection and classification since it can automatically classify incoming data and conduct calculations faster. We introduce Honeypot Early Intrusion Detection System (HEIDS) that detects anomalies and classifies intrusions in IIoT networks using DL methods. The model is designed to detect adversaries attempting to attack IIoT Industrial Control Systems (ICS). The suggested model is implemented using One-dimensional convolutional neural networks (CNN 1D). Due to the importance of industrial services, this system contributes to the enhancement of information security detection in the industrial domain. Finally, this research gives an assessment of the HEIDS datasets of IIoT, utilizing the CNN 1D technique. With this approach, the prediction accuracy of 1.0 was reached.