{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,31]],"date-time":"2026-01-31T09:48:28Z","timestamp":1769852908796,"version":"3.49.0"},"reference-count":34,"publisher":"IEEE","license":[{"start":{"date-parts":[[2023,11,7]],"date-time":"2023-11-07T00:00:00Z","timestamp":1699315200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2023,11,7]],"date-time":"2023-11-07T00:00:00Z","timestamp":1699315200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,11,7]]},"DOI":"10.1109\/nfv-sdn59219.2023.10329594","type":"proceedings-article","created":{"date-parts":[[2023,12,4]],"date-time":"2023-12-04T18:45:54Z","timestamp":1701715554000},"page":"65-71","source":"Crossref","is-referenced-by-count":12,"title":["MERLINS \u2013 Moving Target Defense Enhanced with Deep-RL for NFV In-Depth Security"],"prefix":"10.1109","author":[{"given":"Wissem","family":"Soussi","sequence":"first","affiliation":[{"name":"University of Z&#x00FC;rich UZH,Switzerland"}]},{"given":"Maria","family":"Christopoulou","sequence":"additional","affiliation":[{"name":"National Center for Scientific Research Demokritos (NCSRD),Greece"}]},{"given":"G\u00fcrkan","family":"G\u00fcr","sequence":"additional","affiliation":[{"name":"Zurich University of Applied Sciences,Switzerland"}]},{"given":"Burkhard","family":"Stiller","sequence":"additional","affiliation":[{"name":"University of Z&#x00FC;rich UZH,Switzerland"}]}],"member":"263","reference":[{"key":"ref13","article-title":"Moving target defense for in-vehicle software-defined networking: IP shuffling in network slicing with multiagent deep reinforcement learning","author":"y s","year":"2020","journal-title":"Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications II"},{"key":"ref12","article-title":"General sum markov games for strategic detection of advanced persistent threats using moving target defense in cloud networks","author":"sengupta","year":"2019","journal-title":"Decision and Game Theory for Security"},{"key":"ref34","author":"g\u00fcng\u00f6r","year":"0","journal-title":"UERANSIM"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/NOMS56928.2023.10154367"},{"key":"ref14","year":"2021","journal-title":"Cyberbattlesim"},{"key":"ref31","author":"felten","year":"2022","journal-title":"MORL-baselines Multi-objective reinforcement learning algorithms implementations"},{"key":"ref30","article-title":"Stable-baselines3: Reliable reinforcement learning implementations","author":"a r","year":"2021","journal-title":"Journal of Machine Learning Research"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/ICCCN.2013.6614155"},{"key":"ref33","author":"lee","year":"0","journal-title":"Open5gs"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/DSC50466.2020.00065"},{"key":"ref32","year":"0","journal-title":"Osm Release ELEVEN release notes"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/MCOM.2017.1600951"},{"key":"ref1","article-title":"Network slicing to enable scalability and flexibility in 5G mobile networks","volume":"55","author":"p r","year":"2017","journal-title":"IEEE Communications Magazine"},{"key":"ref17","author":"anagnostopoulos","year":"0","journal-title":"Katana network slice manager"},{"key":"ref16","year":"0","journal-title":"Solidshield Systemic"},{"key":"ref19","year":"0","journal-title":"Greenbone OpenVAS &#x2013; open vulnerability assessment system"},{"key":"ref18","year":"0","journal-title":"MMT - Montimage Monitoring Toolbox"},{"key":"ref24","doi-asserted-by":"crossref","first-page":"529","DOI":"10.1038\/nature14236","article-title":"Human-level control through deep reinforcement learning","volume":"518","author":"mnih","year":"2015","journal-title":"Nature"},{"key":"ref23","article-title":"OpenAI Gym","volume":"abs 1606 1540","author":"brockman","year":"2016","journal-title":"CoRR"},{"key":"ref26","article-title":"Proximal policy optimization algorithms","volume":"abs 1707 6347","author":"schulman","year":"2017","journal-title":"CoRR"},{"key":"ref25","article-title":"Asynchronous methods for deep reinforcement learning","author":"mnih","year":"0","journal-title":"Proceedings of the 33rd International Conference on Machine Learning"},{"key":"ref20","year":"0","journal-title":"National Vulnerability Database (NVD) - Vulnerability Metrics"},{"key":"ref22","article-title":"Q3 Cloud Spending Up Over $11 Billion from 2021 Despite Major Headwinds","year":"2022","journal-title":"Articles"},{"key":"ref21","year":"0","journal-title":"NIST National Vulnerability Database (NVD)"},{"key":"ref28","article-title":"A generalized algorithm for multi-objective reinforcement learning and policy adaptation","author":"yang","year":"2019","journal-title":"Advances in Neural Information Prsyocessing Systems 32"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1016\/j.icte.2020.05.003"},{"key":"ref29","article-title":"Multi-objective reinforcement learning for the expected utility of the return","volume":"2018","author":"roijers","year":"0","journal-title":"Proceedings of the Adaptive and Learning Agents workshop at FAIM"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1016\/j.future.2018.11.045"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/MCOMSTD.211.2000087"},{"key":"ref9","article-title":"MMTD: MANO-based moving target defense for corporate networks","author":"m r","year":"0","journal-title":"2020 World Conference on Computing and Communication Technologies (WCCCT)"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/JSAC.2017.2760418"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/COMST.2017.2705720"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1145\/2663474.2663479"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/EuCNC\/6GSummit51104.2021.9482609"}],"event":{"name":"2023 IEEE Conference on Network Function Virtualization and Software Defined Networks (NFV-SDN)","location":"Dresden, Germany","start":{"date-parts":[[2023,11,7]]},"end":{"date-parts":[[2023,11,9]]}},"container-title":["2023 IEEE Conference on Network Function Virtualization and Software Defined Networks (NFV-SDN)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/10329417\/10329586\/10329594.pdf?arnumber=10329594","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,2,6]],"date-time":"2024-02-06T18:34:45Z","timestamp":1707244485000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10329594\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,7]]},"references-count":34,"URL":"https:\/\/doi.org\/10.1109\/nfv-sdn59219.2023.10329594","relation":{},"subject":[],"published":{"date-parts":[[2023,11,7]]}}}