Version 1
: Received: 3 June 2024 / Approved: 4 June 2024 / Online: 5 June 2024 (07:26:29 CEST)
How to cite:
Alwakeel, A. M.; Alnaim, A. Trust Management and Resource Optimization in Edge and Fog Computing Using the CyberGuard Framework. Preprints2024, 2024060210. https://doi.org/10.20944/preprints202406.0210.v1
Alwakeel, A. M.; Alnaim, A. Trust Management and Resource Optimization in Edge and Fog Computing Using the CyberGuard Framework. Preprints 2024, 2024060210. https://doi.org/10.20944/preprints202406.0210.v1
Alwakeel, A. M.; Alnaim, A. Trust Management and Resource Optimization in Edge and Fog Computing Using the CyberGuard Framework. Preprints2024, 2024060210. https://doi.org/10.20944/preprints202406.0210.v1
APA Style
Alwakeel, A. M., & Alnaim, A. (2024). Trust Management and Resource Optimization in Edge and Fog Computing Using the CyberGuard Framework. Preprints. https://doi.org/10.20944/preprints202406.0210.v1
Chicago/Turabian Style
Alwakeel, A. M. and Abdulrahman Alnaim. 2024 "Trust Management and Resource Optimization in Edge and Fog Computing Using the CyberGuard Framework" Preprints. https://doi.org/10.20944/preprints202406.0210.v1
Abstract
The growing importance of edge and fog computing in the modern IT infrastructure is driven by the rise of decentralized applications. However, resource allocation within these frameworks is challenging due to varying device capabilities and dynamic network conditions. Conventional approaches often result in poor resource use and slowed advancements. This study presents a novel strategy for enhancing resource allocation in edge and fog computing by integrating machine learning with blockchain for reliable trust management. Our proposed framework, called CyberGuard, leverages blockchain’s inherent immutability and decentralization to establish a trustworthy and transparent network for monitoring and verifying edge and fog computing transactions. CyberGuard combines the Trust2Vec model with conventional machine learning models like SVM, KNN, and Random Forests, creating a robust mechanism for assessing trust and security risks. Through detailed optimization and case studies, CyberGuard demonstrates significant improvements in resource allocation efficiency and overall system performance in real-world scenarios. Our results highlight CyberGuard’s effectiveness, evidenced by a remarkable accuracy, precision, recall, and F1-Score of 98.18%, showcasing the transformative potential of our comprehensive approach in edge and fog computing environments.
Keywords
cloud computing; edge computing; fog computing; blockchain; trust management
Subject
Computer Science and Mathematics, Security Systems
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
