White-Box Style Intrusion Detection System Integration into Industrial Metaverses
The Metaverse is an expansive digital realm that merges enhanced physical reality with persistent virtual spaces, encompassing virtual worlds, augmented reality, and the Internet. Its popularity is driven by its ability to offer immersive expe-riences and interactive environments that transcend traditional digital interactions. Within this broad concept, the Industrial Metaverse is particularly significant as it revolutionizes industrial processes through real-time data integration, predictive main-tenance, and advanced simulations, thereby boosting efficiency, reducing costs, and fostering innovation in manufacturing and supply chain management. However, the reliance on Industrial Internet of Things (IIoT) devices introduces significant cyber threats, as these devices often have minimal security measures and can serve as entry points for attackers. Therefore, the security of the Industrial Metaverse is crucial, requiring sophisticated protection mechanisms to safeguard against these evolving cyber threats and ensure the integrity of industrial operations. Furthermore, the transparency of these protection systems is vital for the effective reporting and managing of such attacks. To address the specific security needs of the Industrial Metaverse environment, a White-Box style Intrusion Detection System (IDS) model has been introduced, integrated into the Metaverse through the use of avatars. This model gives promising result as F1-micro of 0.95, reaching the performance of the best existing black-box model in the literature. Additionally, the effectiveness of the developed IDS model is also evaluated by detailed experiments, which not only validate its high accuracy but also underscore its potential to enhance security within complex industrial environments. These comprehensive experiments indicate that the proposed solution could be effective for the cybersecurity of the Industrial Metaverse environment.
Authors
- Vahide Nida Kılıç
- Ali İnan (ORCID: https://orcid.org/0000-0002-3149-1565)
- Yasir Kılıç
Institutions
- Adana Science and Technology University (TR)
Publication Details
- Published
- 2024-10-26
- DOI
- https://doi.org/10.1109/ubmk63289.2024.10773616
- Primary Topic
- Advanced Malware Detection Techniques
- Type
- article
- Field-Weighted Citation Impact
- 0.00