{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,19]],"date-time":"2026-02-19T16:29:10Z","timestamp":1771518550123,"version":"3.50.1"},"reference-count":29,"publisher":"Springer Science and Business Media LLC","issue":"12","license":[{"start":{"date-parts":[[2024,9,6]],"date-time":"2024-09-06T00:00:00Z","timestamp":1725580800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,9,6]],"date-time":"2024-09-06T00:00:00Z","timestamp":1725580800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62276091"],"award-info":[{"award-number":["62276091"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Computing"],"published-print":{"date-parts":[[2024,12]]},"DOI":"10.1007\/s00607-024-01344-4","type":"journal-article","created":{"date-parts":[[2024,9,6]],"date-time":"2024-09-06T15:03:14Z","timestamp":1725634994000},"page":"4015-4038","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Optimization of mitigation deployment using deep reinforcement learning over an enhanced ATT &amp;CK"],"prefix":"10.1007","volume":"106","author":[{"given":"Yingze","family":"Liu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuanbo","family":"Guo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rajiv","family":"Ranjan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dan","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,9,6]]},"reference":[{"key":"1344_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jnca.2020.102969","volume":"178","author":"W Feng","year":"2021","unstructured":"Feng W, Liu C, Cheng B, Chen J (2021) Secure and cost-effective controller deployment in multi-domain sdn with baguette. J Netw Comput Appl 178:102969","journal-title":"J Netw Comput Appl"},{"issue":"1","key":"1344_CR2","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1109\/TDSC.2011.34","volume":"9","author":"N Poolsappasit","year":"2011","unstructured":"Poolsappasit N, Dewri R, Ray I (2011) Dynamic security risk management using bayesian attack graphs. IEEE Trans Dependable Secure Comput 9(1):61\u201374","journal-title":"IEEE Trans Dependable Secure Comput"},{"issue":"2","key":"1344_CR3","doi-asserted-by":"publisher","first-page":"231","DOI":"10.1109\/TDSC.2016.2627033","volume":"16","author":"L Mu\u00f1oz-Gonz\u00e1lez","year":"2017","unstructured":"Mu\u00f1oz-Gonz\u00e1lez L, Sgandurra D, Barr\u00e8re M, Lupu EC (2017) Exact inference techniques for the analysis of bayesian attack graphs. IEEE Trans Dependable Secure Comput 16(2):231\u2013244","journal-title":"IEEE Trans Dependable Secure Comput"},{"key":"1344_CR4","doi-asserted-by":"crossref","unstructured":"Munoz-Gonzalez L, Sgandurra D, Paudice A, Lupu EC (2016) Efficient attack graph analysis through approximate inference. arXiv preprint arXiv:1606.07025","DOI":"10.1145\/3105760"},{"key":"1344_CR5","doi-asserted-by":"crossref","unstructured":"Miehling E, Rasouli M, Teneketzis D (2015) Optimal defense policies for partially observable spreading processes on bayesian attack graphs. In: Proceedings of the Second ACM Workshop on Moving Target Defense, pp 67\u201376","DOI":"10.1145\/2808475.2808482"},{"issue":"3","key":"1344_CR6","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/2480741.2480742","volume":"45","author":"MH Manshaei","year":"2013","unstructured":"Manshaei MH, Zhu Q, Alpcan T, Bac\u015far T, Hubaux J-P (2013) Game theory meets network security and privacy. ACM Comput Surveys (CSUR) 45(3):1\u201339","journal-title":"ACM Comput Surveys (CSUR)"},{"key":"1344_CR7","doi-asserted-by":"publisher","first-page":"193","DOI":"10.1016\/j.future.2020.11.027","volume":"117","author":"A Dahiya","year":"2021","unstructured":"Dahiya A, Gupta BB (2021) A reputation score policy and bayesian game theory based incentivized mechanism for ddos attacks mitigation and cyber defense. Futur Gener Comput Syst 117:193\u2013204","journal-title":"Futur Gener Comput Syst"},{"key":"1344_CR8","doi-asserted-by":"crossref","unstructured":"Huang L, Zhu, Q (2019) In: Al-Shaer, E., Wei, J., Hamlen, K.W., Wang, C. (eds.) Dynamic Bayesian Games for Adversarial and Defensive Cyber Deception, Springer, Cham, pp 75\u201397","DOI":"10.1007\/978-3-030-02110-8_5"},{"issue":"1","key":"1344_CR9","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s40537-020-00318-5","volume":"7","author":"IH Sarker","year":"2020","unstructured":"Sarker IH, Kayes A, Badsha S, Alqahtani H, Watters P, Ng A (2020) Cybersecurity data science: an overview from machine learning perspective. J Big Data 7(1):1\u201329","journal-title":"J Big Data"},{"key":"1344_CR10","doi-asserted-by":"publisher","DOI":"10.1016\/j.jnca.2022.103398","volume":"203","author":"A Mpatziakas","year":"2022","unstructured":"Mpatziakas A, Drosou A, Papadopoulos S, Tzovaras D (2022) Iot threat mitigation engine empowered by artificial intelligence multi-objective optimization. J Netw Comput Appl 203:103398","journal-title":"J Netw Comput Appl"},{"key":"1344_CR11","doi-asserted-by":"crossref","unstructured":"Yousefi M, Mtetwa N, Zhang Y, Tianfield H (2018) A reinforcement learning approach for attack graph analysis. In: 2018 17th IEEE International Conference On Trust, Security And Privacy In Computing And Communications\/12th IEEE International Conference On Big Data Science And Engineering (TrustCom\/BigDataSE), pp 212\u2013217 . IEEE","DOI":"10.1109\/TrustCom\/BigDataSE.2018.00041"},{"key":"1344_CR12","doi-asserted-by":"publisher","DOI":"10.1016\/j.jnca.2020.102662","volume":"163","author":"Parra G De La Torre","year":"2020","unstructured":"De La Torre Parra G, Rad P, Choo KKR, Beebe N (2020) Detecting internet of things attacks using distributed deep learning. J Netw Comput Appl 163:102662","journal-title":"J Netw Comput Appl"},{"key":"1344_CR13","unstructured":"Strom BE, Applebaum A, Miller DP, Nickels KC, Pennington AG, Thomas CB (2018) Mitre att &ck: Design and philosophy. The MITRE Corporation"},{"key":"1344_CR14","doi-asserted-by":"crossref","unstructured":"Han Y, Rubinstein BI, Abraham T, Alpcan T, Vel OD, Erfani S, Hubczenko D, Leckie C, Montague P (2018)Reinforcement learning for autonomous defence in software-defined networking. In: International Conference on Decision and Game Theory for Security, Springer, pp 145\u2013165","DOI":"10.1007\/978-3-030-01554-1_9"},{"issue":"3","key":"1344_CR15","doi-asserted-by":"publisher","first-page":"244","DOI":"10.1109\/JCN.2020.000015","volume":"22","author":"NI Mowla","year":"2020","unstructured":"Mowla NI, Tran NH, Doh I, Chae K (2020) Afrl: adaptive federated reinforcement learning for intelligent jamming defense in fanet. J Commun Netw 22(3):244\u2013258","journal-title":"J Commun Netw"},{"key":"1344_CR16","doi-asserted-by":"publisher","DOI":"10.1016\/j.automatica.2019.108759","volume":"113","author":"AS Leong","year":"2020","unstructured":"Leong AS, Ramaswamy A, Quevedo DE, Karl H, Shi L (2020) Deep reinforcement learning for wireless sensor scheduling in cyber-physical systems. Automatica 113:108759","journal-title":"Automatica"},{"key":"1344_CR17","doi-asserted-by":"crossref","unstructured":"Kwon R, Ashley T, Castleberry J, Mckenzie P, Gourisetti SNG (2020) Cyber threat dictionary using mitre att &ck matrix and nist cybersecurity framework mapping. In: 2020 Resilience Week (RWS), IEEE, pp 106\u2013112","DOI":"10.1109\/RWS50334.2020.9241271"},{"issue":"1","key":"1344_CR18","doi-asserted-by":"publisher","first-page":"157","DOI":"10.1007\/s10270-021-00898-7","volume":"21","author":"W Xiong","year":"2022","unstructured":"Xiong W, Legrand E, \u00c5berg O, Lagerstr\u00f6m R (2022) Cyber security threat modeling based on the mitre enterprise att &ck matrix. Softw Syst Model 21(1):157\u2013177","journal-title":"Softw Syst Model"},{"key":"1344_CR19","doi-asserted-by":"crossref","unstructured":"Al-Shaer R, Spring JM, Christou E (2020) Learning the associations of mitre att & ck adversarial techniques. In: 2020 IEEE Conference on Communications and Network Security (CNS), IEEE, pp 1\u20139","DOI":"10.1109\/CNS48642.2020.9162207"},{"key":"1344_CR20","unstructured":"Christey S, Martin RA (2007) Vulnerability type distributions in cve. Mitre report, May"},{"key":"1344_CR21","doi-asserted-by":"publisher","DOI":"10.1016\/j.physa.2019.121538","volume":"529","author":"H Mo","year":"2019","unstructured":"Mo H, Deng Y (2019) Identifying node importance based on evidence theory in complex networks. Physica A 529:121538","journal-title":"Physica A"},{"key":"1344_CR22","unstructured":"Christodoulou P (2019) Soft actor-critic for discrete action settings. arXiv preprint arXiv:1910.07207"},{"issue":"5","key":"1344_CR23","first-page":"1938","volume":"8","author":"S Mohurle","year":"2017","unstructured":"Mohurle S, Patil M (2017) A brief study of wannacry threat: Ransomware attack 2017. Int J Adv Res Comput Sci 8(5):1938\u20131940","journal-title":"Int J Adv Res Comput Sci"},{"key":"1344_CR24","doi-asserted-by":"crossref","unstructured":"Wang L, Zhang M, Jajodia S, Singhal A, Albanese M (2014) Modeling network diversity for evaluating the robustness of networks against zero-day attacks. In: European Symposium on Research in Computer Security, Springer, pp 494\u2013511","DOI":"10.1007\/978-3-319-11212-1_28"},{"key":"1344_CR25","doi-asserted-by":"crossref","unstructured":"Charpentier A, Boulahia\u00a0Cuppens N, Cuppens F, Yaich R (2022) Deep reinforcement learning-based defense strategy selection. In: Proceedings of the 17th International Conference on Availability, Reliability and Security, pp 1\u201311","DOI":"10.1145\/3538969.3543789"},{"key":"1344_CR26","doi-asserted-by":"publisher","first-page":"1865","DOI":"10.1109\/JSYST.2022.3171240","volume":"17","author":"L Zeng","year":"2022","unstructured":"Zeng L, Yao W, Shuai H, Zhou Y, Ai X, Wen J (2022) Resilience assessment for power systems under sequential attacks using double dqn with improved prioritized experience replay. IEEE Syst J 17:1865","journal-title":"IEEE Syst J"},{"key":"1344_CR27","doi-asserted-by":"crossref","unstructured":"Zhai B, Song F, Huang J, Huang X, Zhou Z, Jin T (2021) Pre-event resilience enhancement strategy for distribution systems based on dueling ddqn. In: 2021 IEEE 4th International Conference on Electronics Technology (ICET), IEEE, pp 527\u2013532","DOI":"10.1109\/ICET51757.2021.9450973"},{"key":"1344_CR28","doi-asserted-by":"crossref","unstructured":"Muhati E. Rawat DB (2021) Asynchronous advantage actor-critic (a3c) learning for cognitive network security. In: 2021 Third IEEE International Conference on Trust, Privacy and Security in Intelligent Systems and Applications (TPS-ISA), IEEE, pp 106\u2013113","DOI":"10.1109\/TPSISA52974.2021.00012"},{"issue":"1","key":"1344_CR29","first-page":"1","volume":"24","author":"Z Hu","year":"2020","unstructured":"Hu Z, Zhu M, Liu P (2020) Adaptive cyber defense against multi-stage attacks using learning-based pomdp. ACM Trans Privacy Security (TOPS) 24(1):1\u201325","journal-title":"ACM Trans Privacy Security (TOPS)"}],"container-title":["Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00607-024-01344-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00607-024-01344-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00607-024-01344-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,7]],"date-time":"2024-11-07T14:08:16Z","timestamp":1730988496000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00607-024-01344-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,9,6]]},"references-count":29,"journal-issue":{"issue":"12","published-print":{"date-parts":[[2024,12]]}},"alternative-id":["1344"],"URL":"https:\/\/doi.org\/10.1007\/s00607-024-01344-4","relation":{},"ISSN":["0010-485X","1436-5057"],"issn-type":[{"value":"0010-485X","type":"print"},{"value":"1436-5057","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,9,6]]},"assertion":[{"value":"20 January 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 August 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 September 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no Conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}