Zarakovitis, C., Klonidis, D., Salazar, Z. et al. (12 more authors) (2021) SANCUS: Multi-layers Vulnerability Management Framework for Cloud-native 5G networks. In: Proceedings of the 16th International Conference on Availability, Reliability and Security. ARES 2021: The 16th International Conference on Availability, Reliability and Security, 17-20 Aug 2021, Vienna, Austria. ACM ISBN 9781450390514
Abstract
Security, Trust and Reliability are crucial issues in mobile 5G networks from both hardware and software perspectives. These issues are of significant importance when considering implementations over distributed environments, i.e., corporate Cloud environment over massively virtualized infrastructures as envisioned in the 5G service provision paradigm. The SANCUS1 solution intends providing a modular framework integrating different engines in order to enable next‐generation 5G system networks to perform automated and intelligent analysis of their firmware images at massive scale, as well as the validation of applications and services. SANCUS also proposes a proactive risk assessment of network applications and services by means of maximising the overall system resilience in terms of security, privacy and reliability. This paper presents an overview of the SANCUS architecture in its current release as well as the pilots use cases that will be demonstrated at the end of the project and used for validating the concepts.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © ACM 2021. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in ARES '21: Proceedings of the 16th International Conference on Availability, Reliability and Security, https://doi.org/10.1145/3465481.3470092 . |
Keywords: | 5G networks, Security, Trust, Reliability, Risk Detection and Mitigation |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 26 Jan 2024 10:46 |
Last Modified: | 01 Feb 2024 13:05 |
Published Version: | https://dl.acm.org/doi/10.1145/3465481.3470092 |
Status: | Published |
Publisher: | ACM |
Identification Number: | 10.1145/3465481.3470092 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:208277 |