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Type | Conference or Workshop Paper |
Scope | Discipline-based scholarship |
Published in Proceedings | Yes |
Title | MERLINS – Moving Target Defense Enhanced with Deep-RL for NFV In-Depth Security |
Organization Unit | |
Authors |
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Presentation Type | paper |
Item Subtype | Original Work |
Refereed | Yes |
Status | Published in final form |
Language |
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ISBN | 979-8-3503-0254-7 |
Page Range | 65 - 71 |
Event Title | 2023 IEEE Conference on Network Function Virtualization and Software Defined Networks (NFV-SDN) |
Event Type | conference |
Event Location | Dresden, Germany |
Event Start Date | November 7 - 2023 |
Event End Date | November 9 - 2023 |
Series Name | Proceedings IEEE Conference on Network Function Virtualization and Software Defined Networks |
Publisher | Institute of Electrical and Electronics Engineers |
Abstract Text | Moving to a multi-cloud environment and service-based architecture, 5G and future 6G networks require additional defensive mechanisms to protect virtualized network resources. This paper presents MERLINS, a novel architecture generating optimal Moving Target Defense (MTD) policies for proactive and reactive security of network slices. By formally modeling telecommunication networks compliant with Network Function Virtualization (NFV) into a multi-objective Markov Decision Process (MOMDP), MERLINS uses deep Reinforcement Learning (deep-RL) to optimize the MTD strategy that considers security, network performance, and service level requirements. Practical experiments on a 5G testbed showcase the feasibility as well as restrictions of MTD operations and the effectiveness in mitigating malware infections. It is observed that multi-objective RL (MORL) algorithms outperform state-of-the-art deep-RL algorithms that scalarize the reward vector of the MOMDP. This improvement by a factor of two leads to a better MTD policy than the baseline static counterpart used for the evaluation. |
Digital Object Identifier | 10.1109/nfv-sdn59219.2023.10329594 |
Other Identification Number | merlin-id:24369 |
PDF File | Download from ZORA |
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