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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 |
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| Additional Information | © 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |