1D-CNN-IDS: 1D CNN-based Intrusion Detection System for IIoT

The demand for Internet of Things (IoT) has seen a rapid increase. These advances have been made possible by technological advances in artificial intelligence, cloud computing, and edge computing. However, these developments present a number of challenges, including cyber threats, security and privacy concerns, and the risk of potential financial losses. For this reason, this study developed a computationally inexpensive one-dimensional convolutional neural network (1DCNN) algorithm for cyber-attack classification. The proposed study achieved an accuracy of 99.90% to classify nine cyber-attacks. Several other performance metrics are evaluated to validate the effectiveness of the proposed scheme. In addition, a comparison has been made with the existing state-of-the-art schemes. The findings of the proposed study can significantly contribute to the development of secure intrusion detection for IIoT systems.