{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T07:05:00Z","timestamp":1774940700894,"version":"3.50.1"},"reference-count":52,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,3,12]],"date-time":"2026-03-12T00:00:00Z","timestamp":1773273600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100018693","name":"Horizon Europe","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100018693","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Computer Networks"],"published-print":{"date-parts":[[2026,5]]},"DOI":"10.1016\/j.comnet.2026.112183","type":"journal-article","created":{"date-parts":[[2026,3,13]],"date-time":"2026-03-13T16:38:31Z","timestamp":1773419911000},"page":"112183","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Multi-Objective and deep Q-Learning for countermeasure selection in 5G intrusion response systems"],"prefix":"10.1016","volume":"281","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1360-6952","authenticated-orcid":false,"given":"Arash","family":"Bozorgchenani","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9928-6963","authenticated-orcid":false,"given":"Dimitris","family":"Manolakis","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5337-161X","authenticated-orcid":false,"given":"Antonios","family":"Lalas","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"key":"10.1016\/j.comnet.2026.112183_bib0001","doi-asserted-by":"crossref","first-page":"7958","DOI":"10.1109\/ACCESS.2018.2799603","article-title":"Self-Adaptive scheduling of base transceiver stations in green 5G networks","volume":"6","author":"Dutta","year":"2018","journal-title":"IEEE Access"},{"issue":"5","key":"10.1016\/j.comnet.2026.112183_bib0002","doi-asserted-by":"crossref","first-page":"3510","DOI":"10.1109\/TII.2021.3113130","article-title":"SDN-Assisted Safety message dissemination framework for vehicular critical energy infrastructure","volume":"18","author":"Prathiba","year":"2022","journal-title":"IEEE Trans. Ind. Inf."},{"key":"10.1016\/j.comnet.2026.112183_bib0003","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1016\/j.jnca.2017.10.017","article-title":"Security for 4G and 5G cellular networks: a survey of existing authentication and privacy-preserving schemes","volume":"101","author":"Ferrag","year":"2018","journal-title":"J. Netw. Comput. Appl."},{"key":"10.1016\/j.comnet.2026.112183_bib0004","unstructured":"Christopher J. Alberts, Audrey J. Dorofee, James F. Stevens, Carol Woody, OCTAVE-S Implementation Guide, 2005, 10.1184\/R1\/6575852.v1."},{"key":"10.1016\/j.comnet.2026.112183_bib0005","doi-asserted-by":"crossref","DOI":"10.1016\/j.comnet.2023.110051","article-title":"Novel modeling and optimization for joint cybersecurity-vs-Qos intrusion detection mechanisms in 5G networks","volume":"237","author":"Bozorgchenani","year":"2023","journal-title":"Comput. Netw."},{"issue":"2","key":"10.1016\/j.comnet.2026.112183_bib0006","doi-asserted-by":"crossref","first-page":"395","DOI":"10.1109\/TPDS.2013.211","article-title":"RRE: A game-Theoretic intrusion response and recovery engine","volume":"25","author":"Zonouz","year":"2014","journal-title":"IEEE Trans. Parallel Distrib. Syst."},{"key":"10.1016\/j.comnet.2026.112183_bib0007","first-page":"1","article-title":"Intrusion response systems for the 5G networks and beyond: a new joint security-vs-Qos optimization approach","author":"Bozorgchenani","year":"2024","journal-title":"IEEE Trans. Network Sci. Eng."},{"key":"10.1016\/j.comnet.2026.112183_bib0008","doi-asserted-by":"crossref","first-page":"60971","DOI":"10.1109\/ACCESS.2021.3074021","article-title":"A bio-Inspired reaction against cyberattacks: AIS-Powered optimal countermeasures selection","volume":"9","author":"Nespoli","year":"2021","journal-title":"IEEE Access"},{"key":"10.1016\/j.comnet.2026.112183_bib0009","series-title":"Proceedings of the 16Th International Conference on Availability, Reliability and Security","article-title":"AISGA: Multi-Objective parameters optimization for countermeasures selection through genetic algorithm","author":"NespoliPantaleone and Gomez Marmol, Felix and Kambourakis, Georgios","year":"2021"},{"key":"10.1016\/j.comnet.2026.112183_bib0010","series-title":"ICC 2020 - 2020 IEEE International Conference on Communications (ICC)","first-page":"1","article-title":"Decision-Making for intrusion response: which, where, in what order, and how long?","author":"Guo","year":"2020"},{"issue":"5","key":"10.1016\/j.comnet.2026.112183_bib0011","doi-asserted-by":"crossref","first-page":"2544","DOI":"10.1109\/TII.2018.2866445","article-title":"A dynamic decision-Making approach for intrusion response in industrial control systems","volume":"15","author":"LiXuan and Zhou, Chunjie and Tian, Yu-Chu and Qin, Yuanqing","year":"2019","journal-title":"IEEE Trans. Ind. Inf."},{"key":"10.1016\/j.comnet.2026.112183_bib0012","doi-asserted-by":"crossref","DOI":"10.1016\/j.cose.2020.101927","article-title":"Dynamic countermeasures selection for multi-path attacks","volume":"97","author":"Li","year":"2020","journal-title":"Comput. Secur."},{"key":"10.1016\/j.comnet.2026.112183_bib0013","series-title":"2020 IEEE 33Rd Computer Security Foundations Symposium (CSF)","first-page":"395","article-title":"Exploiting attack-defense trees to find an optimal set of countermeasures","author":"Fila","year":"2020"},{"key":"10.1016\/j.comnet.2026.112183_bib0014","series-title":"2021 IEEE 34Th Computer Security Foundations Symposium (CSF)","first-page":"1","article-title":"Heuristic approach for countermeasure selection using attack graphs","author":"Stan","year":"2021"},{"key":"10.1016\/j.comnet.2026.112183_bib0015","series-title":"2019 IEEE 4Th International Workshops on Foundations and Applications of Self* Systems","first-page":"158","article-title":"A performance evaluation of deep reinforcement learning for model-Based intrusion response","author":"Iannucci","year":"2019"},{"key":"10.1016\/j.comnet.2026.112183_bib0016","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1016\/j.future.2020.03.018","article-title":"A hybrid model-free approach for the near-optimal intrusion response control of non-stationary systems","volume":"109","author":"Iannucci","year":"2020","journal-title":"Future Generat. Comput. Syst."},{"key":"10.1016\/j.comnet.2026.112183_bib0017","article-title":"Fog node intrusion detection and response based on SVMIF and INSGA-II algorithm","volume":"7","author":"Luo","year":"2025","journal-title":"Syst. Soft Comput."},{"key":"10.1016\/j.comnet.2026.112183_bib0018","doi-asserted-by":"crossref","DOI":"10.1016\/j.cose.2024.104008","article-title":"REACT: Autonomous intrusion response system for intelligent vehicles","volume":"145","author":"Hamad","year":"2024","journal-title":"Comput. Secur."},{"key":"10.1016\/j.comnet.2026.112183_bib0019","doi-asserted-by":"crossref","DOI":"10.1016\/j.cose.2022.102984","article-title":"Intrusion response systems for cyber-physical systems: a comprehensive survey","volume":"124","author":"Bashendy","year":"2023","journal-title":"Comput. Secur."},{"issue":"11","key":"10.1016\/j.comnet.2026.112183_bib0020","doi-asserted-by":"crossref","first-page":"2210","DOI":"10.1109\/JSAC.2012.121213","article-title":"Coordination in network security games: a monotone comparative statics approach","volume":"30","author":"Lelarge","year":"2012","journal-title":"IEEE J. Sel. Areas Commun."},{"issue":"3","key":"10.1016\/j.comnet.2026.112183_bib0021","doi-asserted-by":"crossref","first-page":"493","DOI":"10.1016\/j.dss.2011.02.013","article-title":"Decision support for cybersecurity risk planning","volume":"51","author":"Rees","year":"2011","journal-title":"Decis. Support Syst."},{"issue":"1","key":"10.1016\/j.comnet.2026.112183_bib0022","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1016\/j.ejor.2019.09.017","article-title":"Cybersecurity investments in the supply chain: coordination and a strategic attacker","volume":"282","author":"Simon","year":"2020","journal-title":"Eur. J. Oper. Res."},{"key":"10.1016\/j.comnet.2026.112183_bib0023","doi-asserted-by":"crossref","DOI":"10.1016\/j.dss.2019.05.009","article-title":"Socially optimal IT investment for cybersecurity","volume":"122","author":"Paul","year":"2019","journal-title":"Decis. Support Syst."},{"issue":"1","key":"10.1016\/j.comnet.2026.112183_bib0024","doi-asserted-by":"crossref","first-page":"349","DOI":"10.1016\/j.ejor.2020.09.013","article-title":"Decision support model for cybersecurity risk planning: a two-stage stochastic programming framework featuring firms, government, and attacker","volume":"291","author":"Paul","year":"2021","journal-title":"Eur. J. Oper. Res."},{"key":"10.1016\/j.comnet.2026.112183_bib0025","doi-asserted-by":"crossref","DOI":"10.1016\/j.cose.2020.102121","article-title":"Optimisation of cyber insurance coverage with selection of cost effective security controls","volume":"101","author":"Uuganbayar","year":"2021","journal-title":"Comput. Secur."},{"issue":"2","key":"10.1016\/j.comnet.2026.112183_bib0026","doi-asserted-by":"crossref","first-page":"1361","DOI":"10.1109\/COMST.2017.2781126","article-title":"Optimal countermeasures selection against cyber attacks: a comprehensive survey on reaction frameworks","volume":"20","author":"Nespoli","year":"2018","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"10.1016\/j.comnet.2026.112183_bib0027","doi-asserted-by":"crossref","DOI":"10.1016\/j.comnet.2023.109873","article-title":"PEDDA: Practical and effective detection of distributed attacks on enterprise networks via progressive multi-stage inference","volume":"233","author":"Lyu","year":"2023","journal-title":"Comput. Netw."},{"key":"10.1016\/j.comnet.2026.112183_bib0028","doi-asserted-by":"crossref","DOI":"10.1016\/j.sysarc.2022.102722","article-title":"IDERES: Intrusion detection and response system using machine learning and attack graphs","volume":"131","author":"Rose","year":"2022","journal-title":"J. Syst. Archit."},{"key":"10.1016\/j.comnet.2026.112183_bib0029","series-title":"2022 6Th International Conference on Cryptography, Security and Privacy (CSP)","first-page":"124","article-title":"From machine learning based intrusion detection to cost sensitive intrusion response","author":"Hussain","year":"2022"},{"issue":"20","key":"10.1016\/j.comnet.2026.112183_bib0030","doi-asserted-by":"crossref","first-page":"33312","DOI":"10.1109\/JIOT.2024.3426054","article-title":"A hierarchical unmanned aerial vehicle network intrusion detection and response approach based on immune vaccine distribution","volume":"11","author":"Chen","year":"2024","journal-title":"IEEE Internet Things J."},{"key":"10.1016\/j.comnet.2026.112183_bib0031","doi-asserted-by":"crossref","first-page":"129856","DOI":"10.1109\/ACCESS.2025.3585956","article-title":"ASIC Design for real-Time CAN-Bus intrusion detection and prevention system using random forest","volume":"13","author":"Lee","year":"2025","journal-title":"IEEE Access"},{"issue":"2","key":"10.1016\/j.comnet.2026.112183_bib0032","doi-asserted-by":"crossref","first-page":"1984","DOI":"10.1109\/JSYST.2019.2945555","article-title":"Autonomic intrusion detection and response using big data","volume":"14","author":"Vieira","year":"2020","journal-title":"IEEE Syst. J."},{"key":"10.1016\/j.comnet.2026.112183_bib0033","doi-asserted-by":"crossref","first-page":"148577","DOI":"10.1109\/ACCESS.2024.3460743","article-title":"Design of intrusion detection and response mechanism for power grid SCADA based on improved LSTM and FNN","volume":"12","author":"Huang","year":"2024","journal-title":"IEEE Access"},{"key":"10.1016\/j.comnet.2026.112183_bib0034","series-title":"2025 5Th Intelligent Cybersecurity Conference (ICSC)","first-page":"84","article-title":"AI-Powered Threat detection and response: leveraging machine learning for real-Time intrusion detection systems (IDS) using network traffic data","author":"El-Hajj","year":"2025"},{"key":"10.1016\/j.comnet.2026.112183_bib0035","unstructured":"Sancus Project, Sancus project, 2023, [Online], https:\/\/sancus-project.eu\/."},{"issue":"4","key":"10.1016\/j.comnet.2026.112183_bib0036","doi-asserted-by":"crossref","first-page":"198","DOI":"10.1109\/TDSC.2013.8","article-title":"NICE: Network intrusion detection and countermeasure selection in virtual network systems","volume":"10","author":"Chung","year":"2013","journal-title":"IEEE Trans. Dependable Secure Comput."},{"key":"10.1016\/j.comnet.2026.112183_bib0037","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1016\/j.future.2020.09.002","article-title":"Autonomous mitigation of cyber risks in the cyber-physical systems","volume":"115","author":"Kholidy","year":"2021","journal-title":"Future Generat. Comput. Syst."},{"key":"10.1016\/j.comnet.2026.112183_bib0038","series-title":"2015 IEEE 31St International Conference on Data Engineering","first-page":"879","article-title":"Size-Constrained weighted set cover","author":"Golab","year":"2015"},{"issue":"5","key":"10.1016\/j.comnet.2026.112183_bib0039","doi-asserted-by":"crossref","first-page":"1500","DOI":"10.1109\/LCOMM.2021.3055535","article-title":"On-Demand service deployment strategies for fog-as-a-Service scenarios","volume":"25","author":"Bozorgchenani","year":"2021","journal-title":"IEEE Commun. Lett."},{"key":"10.1016\/j.comnet.2026.112183_bib0040","series-title":"Proceedings of the 37Th International Conference on Machine Learning","article-title":"Prediction-Guided multi-Objective reinforcement learning for continuous robot control","author":"Xu","year":"2020"},{"issue":"3","key":"10.1016\/j.comnet.2026.112183_bib0041","doi-asserted-by":"crossref","first-page":"219","DOI":"10.1080\/09528130903157377","article-title":"Reinforcement learning via approximation of the Q-function","volume":"22","author":"Langlois","year":"2010","journal-title":"J. Exp. Theor. Artif. Intell."},{"issue":"10","key":"10.1016\/j.comnet.2026.112183_bib0042","doi-asserted-by":"crossref","first-page":"2992","DOI":"10.1109\/TMC.2020.2994232","article-title":"Multi-Objective computation sharing in energy and delay constrained mobile edge computing environments","volume":"20","author":"Bozorgchenani","year":"2021","journal-title":"IEEE Trans. Mob. Comput."},{"issue":"2","key":"10.1016\/j.comnet.2026.112183_bib0043","doi-asserted-by":"crossref","first-page":"182","DOI":"10.1109\/4235.996017","article-title":"A fast and elitist multiobjective genetic algorithm: NSGA-II","volume":"6","author":"Deb","year":"2002","journal-title":"IEEE Trans. Evol. Comput."},{"key":"10.1016\/j.comnet.2026.112183_bib0044","series-title":"AI 2008: Advances in Artificial Intelligence","first-page":"372","article-title":"On the limitations of scalarisation for multi-objective reinforcement learning of pareto fronts","author":"Vamplew","year":"2008"},{"key":"10.1016\/j.comnet.2026.112183_bib0045","series-title":"2013 IEEE Symposium on Adaptive Dynamic Programming and Reinforcement Learning (ADPRL)","first-page":"191","article-title":"Scalarized multi-objective reinforcement learning: novel design techniques","author":"Van Moffaert","year":"2013"},{"issue":"7540","key":"10.1016\/j.comnet.2026.112183_bib0046","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"},{"issue":"8","key":"10.1016\/j.comnet.2026.112183_bib0047","doi-asserted-by":"crossref","first-page":"3779","DOI":"10.1109\/TNNLS.2021.3121870","article-title":"Deep reinforcement learning for cyber security","volume":"34","author":"Nguyen","year":"2023","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"10.1016\/j.comnet.2026.112183_bib0048","unstructured":"A. Bozorgchenani, et al., Report on definition and modelling of MiU, 2023, Deliverable 4.2 EU SANCUS Project, https:\/\/sancus-project.eu\/wp-content\/uploads\/2024\/01\/SANCUS_D4.2-Final.pdf."},{"key":"10.1016\/j.comnet.2026.112183_bib0049","unstructured":"M. Abadi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen, C. Citro, G.S. Corrado, A. Davis, J. Dean, M. Devin, S. Ghemawat, I. Goodfellow, A. Harp, G. Irving, M. Isard, Y. Jia, R. Jozefowicz, L. Kaiser, M. Kudlur, J. Levenberg, D. Man\u00e9, R. Monga, S. Moore, D. Murray, C. Olah, M. Schuster, J. Shlens, B. Steiner, I. Sutskever, K. Talwar, P. Tucker, V. Vanhoucke, V. Vasudevan, F. Vi\u00e9gas, O. Vinyals, P. Warden, M. Wattenberg, M. Wicke, Y. Yu, X. Zheng, TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems, 2015, Software available from tensorflow.org, https:\/\/www.tensorflow.org\/."},{"key":"10.1016\/j.comnet.2026.112183_bib0050","series-title":"2015 IEEE International Conference on Computer Vision (ICCV)","first-page":"1026","article-title":"Delving deep into rectifiers: surpassing human-Level performance on imagenet classification","author":"He","year":"2015"},{"key":"10.1016\/j.comnet.2026.112183_bib0051","series-title":"International Conference on Learning Representations (ICLR)","article-title":"Adam: a method for stochastic optimization","author":"Kingma","year":"2015"},{"issue":"1","key":"10.1016\/j.comnet.2026.112183_bib0052","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1007\/s11416-022-00439-w","article-title":"Intelligence in security countermeasures selection","volume":"19","author":"Tamjidi","year":"2023","journal-title":"J. Comput. Virol. Hack. Techni."}],"container-title":["Computer Networks"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1389128626001957?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1389128626001957?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T05:33:28Z","timestamp":1774935208000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1389128626001957"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,5]]},"references-count":52,"alternative-id":["S1389128626001957"],"URL":"https:\/\/doi.org\/10.1016\/j.comnet.2026.112183","relation":{},"ISSN":["1389-1286"],"issn-type":[{"value":"1389-1286","type":"print"}],"subject":[],"published":{"date-parts":[[2026,5]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Multi-Objective and deep Q-Learning for countermeasure selection in 5G intrusion response systems","name":"articletitle","label":"Article Title"},{"value":"Computer Networks","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.comnet.2026.112183","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 The Author(s). Published by Elsevier B.V.","name":"copyright","label":"Copyright"}],"article-number":"112183"}}