{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,2]],"date-time":"2026-01-02T07:49:43Z","timestamp":1767340183955,"version":"build-2065373602"},"reference-count":49,"publisher":"Institution of Engineering and Technology (IET)","issue":"3","license":[{"start":{"date-parts":[[2021,11,27]],"date-time":"2021-11-27T00:00:00Z","timestamp":1637971200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62002383","62072467"],"award-info":[{"award-number":["62002383","62072467"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["ietresearch.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["IET Information Security"],"published-print":{"date-parts":[[2022,5]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Defensive deception is emerging to reveal stealthy attackers by presenting intentionally falsified information. To implement it in the increasing dynamic and complex cloud, major concerns remain about the establishment of precise adversarial model and the adaptive decoy placement strategy. However, existing studies do not fulfil both issues because of (1) the insufficiency on extracting potential threats in virtualisation technique, (2) the inadequate learning on the agility of target environment, and (3) the lack of measurement for placement strategy. In this study, an optimal defensive deception framework is proposed for the container based\u2010cloud. The System Risk Graph (SRG) is formalised to depict an updatable adversarial model with the automatic orchestration platform. Afterwards, a Deep Reinforcement Learning (DRL) model is trained based on SRG. The well\u2010trained DRL agent generates optimal placement strategies for the orchestration platform to distribute decoys and deceptive routings. Lastly, the coefficient of deception, , is defined to evaluate the effectiveness of placement strategy. Simulation results show that the proposed method increases  by 30.22%, and increase the detection ratio on the random walker attacker and persistent attacker by 30.69% and 51.10%, respectively.<\/jats:p>","DOI":"10.1049\/ise2.12050","type":"journal-article","created":{"date-parts":[[2021,11,27]],"date-time":"2021-11-27T10:39:10Z","timestamp":1638009550000},"page":"178-192","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["An optimal defensive deception framework for the container\u2010based cloud with deep reinforcement learning"],"prefix":"10.1049","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5737-7309","authenticated-orcid":false,"given":"Huanruo","family":"Li","sequence":"first","affiliation":[{"name":"Department of Computer Science National Digital Switching System Engineering and Technological Research Center  Zhengzhou Henan China"}]},{"given":"Yunfei","family":"Guo","sequence":"additional","affiliation":[{"name":"Department of Computer Science National Digital Switching System Engineering and Technological Research Center  Zhengzhou Henan China"}]},{"given":"Penghao","family":"Sun","sequence":"additional","affiliation":[{"name":"Department of Computer Science National Digital Switching System Engineering and Technological Research Center  Zhengzhou Henan China"},{"name":"Department of Communication Technologies Academy of Military Science  Beijing China"}]},{"given":"Yawen","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Computer Science National Digital Switching System Engineering and Technological Research Center  Zhengzhou Henan China"}]},{"given":"Shumin","family":"Huo","sequence":"additional","affiliation":[{"name":"Department of Computer Science National Digital Switching System Engineering and Technological Research Center  Zhengzhou Henan 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