Industry 4.0 affects all components of the modern industry value chain. The accelerating use of the Internet and the convergence of industrial and operational networks constantly increase the need for secure industrial communication solutions. Therefore, "multilevel industrial cyber protection" is critical to Industry 4.0. In general, industrial protection refers to safeguarding information and data and the intellectual property rights of production processes related to the overall industry environment. The availability, integrity, and confidentiality of systems must be maintained. The goal challenge is the best possible protection from attacks and threats which create immediate financial damage and other risks in the industry (reputation, etc.). Based on the Defense-in-Depth strategy, a holistic, multilayered, and in-depth protection of industrial systems is developed in this paper. Specifically, a Semi-Self-Supervised Intrusion Detection System (S3IDS) is proposed, which combines advanced machine learning techniques for industrial data noise reduction to automate the discovery and separation of classes, which are essentially equivalent to cyber-related anomalies. As demonstrated by a mathematical simulation based on computational number theory and specifically on the concept of the single object, the proposed S3IDS learns to accurately reconstruct samples to predict the nature of an anomaly created directly by the industrial ecosystem.
Copyright © 2022 Fuchuan Ye and Weiqiong Zhao.