Centralising Monoids with Low-Arity Witnesses on a Four-Element Set

Behrisch, Mike; Vargas-García, Edith

doi:10.3390/sym13081471

Cited by 5 publications

(1 citation statement)

References 24 publications

(71 reference statements)

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“…So, the data set is obtained as a subscale of the signal processing process for analyzing and manipulating the physical quantities that define the given problem of information systems security [ 42 ]. Thus, when Δ ⟶ 0, the duration of the pulse decreases and its height increases, but the area remains constant and equal to the unit.…”

Section: Proposed S3idsmentioning

confidence: 99%

A Semi-Self-Supervised Intrusion Detection System for Multilevel Industrial Cyber Protection

Zhao

2022

Computational Intelligence and Neuroscience

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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.

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Section: Proposed S3idsmentioning

confidence: 99%