{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,9]],"date-time":"2026-06-09T15:48:33Z","timestamp":1781020113264,"version":"3.54.1"},"reference-count":63,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2023,6,27]],"date-time":"2023-06-27T00:00:00Z","timestamp":1687824000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,6,27]],"date-time":"2023-06-27T00:00:00Z","timestamp":1687824000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Peer-to-Peer Netw. Appl."],"published-print":{"date-parts":[[2023,8]]},"DOI":"10.1007\/s12083-023-01507-8","type":"journal-article","created":{"date-parts":[[2023,6,27]],"date-time":"2023-06-27T09:03:52Z","timestamp":1687856632000},"page":"1993-2021","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":30,"title":["TEA-EKHO-IDS: An intrusion detection system for industrial CPS with trustworthy explainable AI and enhanced krill herd optimization"],"prefix":"10.1007","volume":"16","author":[{"given":"S.","family":"Sivamohan","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"S. S.","family":"Sridhar","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"S.","family":"Krishnaveni","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,6,27]]},"reference":[{"issue":"1","key":"1507_CR1","doi-asserted-by":"publisher","first-page":"389","DOI":"10.1109\/COMST.2019.2959013","volume":"22","author":"Y Zhou","year":"2020","unstructured":"Zhou Y, Yu FR, Chen J, Kuo Y (2020) Cyber-physical-social systems: A state-of- the art survey, challenges and opportunities. IEEE Commun Surv Tutor 22(1):389\u2013425","journal-title":"IEEE Commun Surv Tutor"},{"issue":"Special Centenn","key":"1507_CR2","doi-asserted-by":"publisher","first-page":"1287","DOI":"10.1109\/JPROC.2012.2189792","volume":"100","author":"K-D Kim","year":"2012","unstructured":"Kim K-D, Kumar PR (2012) Cyber\u2013physical systems: A perspective at the centennial. Proc IEEE 100(Special Centennial Issue):1287\u20131308","journal-title":"Proc IEEE"},{"issue":"5","key":"1507_CR3","doi-asserted-by":"publisher","first-page":"1013","DOI":"10.1109\/JPROC.2015.2497161","volume":"104","author":"C Lu","year":"2016","unstructured":"Lu C et al (2016) Real-time wireless sensor-actuator networks for industrial cyber-physical systems. Proc IEEE 104(5):1013\u20131024","journal-title":"Proc IEEE"},{"issue":"6","key":"1507_CR4","doi-asserted-by":"publisher","first-page":"4177","DOI":"10.1109\/TII.2019.2942190","volume":"16","author":"Y Lu","year":"2020","unstructured":"Lu Y, Huang X, Dai Y, Maharjan S, Zhang Y (2020) Blockchain and federated Learning for privacy-preserved data sharing in industrial IoT. IEEE Trans Ind Informat 16(6):4177\u20134186","journal-title":"IEEE Trans Ind Informat"},{"key":"1507_CR5","doi-asserted-by":"publisher","first-page":"32","DOI":"10.1016\/j.jpdc.2016.12.012","volume":"103","author":"B Li","year":"2017","unstructured":"Li B, Lu R, Wang W, Choo K-KR (2017) Distributed host-based collaborative detection for false data injection attacks in smart grid cyber-physical system. J Parallel Distrib Comput 103:32\u201341","journal-title":"J Parallel Distrib Comput"},{"issue":"3","key":"1507_CR6","doi-asserted-by":"publisher","first-page":"352","DOI":"10.1109\/TETC.2014.2386615","volume":"3","author":"C Chen","year":"2015","unstructured":"Chen C, Yan J, Lu N, Wang Y, Yang X, Guan X (2015) Ubiquitous monitoring for industrial cyber-physical systems over relay-assisted wireless sensor networks. IEEE Trans Emerg Topics Comput 3(3):352\u2013362","journal-title":"IEEE Trans Emerg Topics Comput"},{"key":"1507_CR7","doi-asserted-by":"crossref","unstructured":"Lee EA (2008) Cyber physical systems: design challenges. In 2008 11th IEEE International Symposium on Object and Component Oriented Real-Time Distributed Computing (ISORC). IEEE, Orlando, FL, USA, 363\u2013369","DOI":"10.1109\/ISORC.2008.25"},{"issue":"1","key":"1507_CR8","doi-asserted-by":"publisher","first-page":"746","DOI":"10.1109\/COMST.2019.2944748","volume":"22","author":"MU Hassan","year":"2020","unstructured":"Hassan MU, Rehmani MH, Chen J (2020) Differential privacy techniques for cyber- Physical systems: A survey. IEEE Commun Surv Tutor 22(1):746\u2013789","journal-title":"IEEE Commun Surv Tutor"},{"key":"1507_CR9","doi-asserted-by":"publisher","first-page":"78238","DOI":"10.1109\/ACCESS.2018.2884906","volume":"6","author":"Xu Hansong","year":"2018","unstructured":"Hansong Xu, Wei Yu, Griith D, Golmie N (2018) A survey on Industrial Internet of Things: A cyber-physical systems perspective. IEEE Access 6:78238\u201378259","journal-title":"IEEE Access"},{"key":"1507_CR10","doi-asserted-by":"crossref","unstructured":"Yamin MM, Katt B, Gkioulos V (2020) Cyber ranges and security testbeds: Scenarios, functions, tools and architecture. Comput Secur 88","DOI":"10.1016\/j.cose.2019.101636"},{"key":"1507_CR11","doi-asserted-by":"crossref","unstructured":"Yu W, Dillon T, Mostafa F, Rahayu W, Liu Y (2019) Implementation of industrial cyber physical system: Challenges and solutions. IEEE Int Conf Ind Cyber Phys Syst (ICPS). IEEE, Taipei, Taiwan, 173\u2013178","DOI":"10.1109\/ICPHYS.2019.8780271"},{"issue":"5","key":"1507_CR12","doi-asserted-by":"publisher","first-page":"1058","DOI":"10.1109\/JPROC.2015.2503119","volume":"104","author":"X Yu","year":"2016","unstructured":"Yu X, Xue Y (2016) Smart grids: a cyber\u2013physical systems perspective. Proc IEEE 104(5):1058\u20131070","journal-title":"Proc IEEE"},{"key":"1507_CR13","doi-asserted-by":"publisher","first-page":"29575","DOI":"10.1109\/ACCESS.2020.2972627","volume":"8","author":"T Su","year":"2020","unstructured":"Su T, Sun H, Zhu J, Wang S, Li Y (2020) BAT: Deep learning methods on network intrusion detection using NSL-KDD dataset. IEEE Access 8:29575\u201329585","journal-title":"IEEE Access"},{"key":"1507_CR14","doi-asserted-by":"publisher","DOI":"10.1016\/j.comnet.2019.107042","volume":"168","author":"W Elmasry","year":"2020","unstructured":"Elmasry W, Akbulut A, Zaim AH (2020) Evolving deep learning architectures for network intrusion detection using a double PSO metaheuristic. Comput Netw 168:107042","journal-title":"Comput Netw"},{"issue":"5","key":"1507_CR15","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3453155","volume":"54","author":"Y Luo","year":"2021","unstructured":"Luo Y, Xiao Y, Cheng L, Peng G, Yao D (2021) Deep learning-based anomaly detection in cyber-physical systems: Progress and opportunities. ACM Comput Surv (CSUR) 54(5):1\u201336","journal-title":"ACM Comput Surv (CSUR)"},{"key":"1507_CR16","doi-asserted-by":"crossref","unstructured":"Faker O, Dogdu E (2019) Intrusion detection using big data and deep learning techniques. Proc ACM Southeast Conf 86\u201393","DOI":"10.1145\/3299815.3314439"},{"key":"1507_CR17","doi-asserted-by":"publisher","DOI":"10.1016\/j.adhoc.2019.101974","volume":"95","author":"J Zhang","year":"2019","unstructured":"Zhang J, Li F, Zhang H, Li R, Li Y (2019) Intrusion detection system using deep learning for in-vehicle security. Ad Hoc Netw 95:101974","journal-title":"Ad Hoc Netw"},{"issue":"1","key":"1507_CR18","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s42400-020-00065-3","volume":"4","author":"CM Ahmed","year":"2021","unstructured":"Ahmed CM, Mathur A (2021) Machine learning for intrusion detection in industrial control systems: Challenges and lessons from experimental evaluation. Cybersecurity 4(1):1\u201312","journal-title":"Cybersecurity"},{"key":"1507_CR19","first-page":"1","volume":"2013","author":"SH Ahmed","year":"2013","unstructured":"Ahmed SH, Kim G, Kim D (2013) Cyber Physical System: Architecture, applications and research challenges. IFIP Wireless Days (WD) 2013:1\u20135","journal-title":"IFIP Wireless Days (WD)"},{"key":"1507_CR20","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2018\/1728303","volume":"2018","author":"S Venkatraman","year":"2018","unstructured":"Venkatraman S, Alazab M (2018) Use of data visualisation for zeroday malware detection. Secur Commun Netw 2018:1\u201313","journal-title":"Secur Commun Netw"},{"key":"1507_CR21","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1016\/j.mfglet.2014.12.001","volume":"3","author":"J Lee","year":"2015","unstructured":"Lee J, Bagheri B, Kao HA (2015) A Cyber-Physical Systems architecture for Industry 4.0-based manufacturing systems. Manuf Lett 3:18\u201323","journal-title":"Manuf Lett"},{"issue":"12","key":"1507_CR22","doi-asserted-by":"publisher","first-page":"6472","DOI":"10.1109\/TII.2019.2917693","volume":"15","author":"B Jiang","year":"2019","unstructured":"Jiang B, Yang J, Ding G, Wang H (2019) Cyber-physical security design in multimedia data cache resource allocation for industrial networks. IEEE Trans Industr Inform 15(12):6472\u20136480","journal-title":"IEEE Trans Industr Inform"},{"key":"1507_CR23","doi-asserted-by":"crossref","unstructured":"Lalithadevi B, Krishnaveni S (2022) Efficient disease risk prediction based on deep learning approach. Int Conf Comput Methodol Commun (ICCMC) 1197\u20131204. IEEE","DOI":"10.1109\/ICCMC53470.2022.9753851"},{"key":"1507_CR24","doi-asserted-by":"crossref","unstructured":"Feng C, Li T, Chana D (2017) Multi-level anomaly detection in industrial control systems via package signatures and lstm networks. Proc Ann EEE\/IFIP Int Conf Dependable Syst Netw 261\u2013272","DOI":"10.1109\/DSN.2017.34"},{"issue":"4","key":"1507_CR25","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/2542049","volume":"46","author":"R Mitchell","year":"2014","unstructured":"Mitchell R, Chen IR (2014) A survey of intrusion detection techniques for cyber-physical systems. ACM Comput Surv (CSUR) 46(4):1\u201329","journal-title":"ACM Comput Surv (CSUR)"},{"issue":"11","key":"1507_CR26","doi-asserted-by":"publisher","first-page":"e6838","DOI":"10.1002\/cpe.6838","volume":"34","author":"S Krishnaveni","year":"2022","unstructured":"Krishnaveni S, Sivamohan S, Sridhar S, Prabhakaran S (2022) Network intrusion detection based on ensemble classification and feature selection method for cloud computing. Concurr Comput Pract Exp 34(11):e6838","journal-title":"Concurr Comput Pract Exp"},{"key":"1507_CR27","first-page":"727","volume":"2020","author":"N Moustafa","year":"2020","unstructured":"Moustafa N, Ahmed M, Ahmed S (2020) Data analytics-enabled intrusion detection: Evaluations of ToN IoT linux datasets. TrustCom 2020:727\u2013735","journal-title":"TrustCom"},{"key":"1507_CR28","doi-asserted-by":"publisher","first-page":"82","DOI":"10.1016\/j.inffus.2019.12.012","volume":"58","author":"AB Arrieta","year":"2020","unstructured":"Arrieta AB et al (2020) Explainable artificial intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI. Inf Fusion 58:82\u2013115","journal-title":"Inf Fusion"},{"key":"1507_CR29","unstructured":"Gunning D (2016) Explainable artificial intelligence (XAI): Technical report defense advanced research projects agency DARPA-BAA-16\u201353. Arlington, TX, USA: DARPA"},{"issue":"5","key":"1507_CR30","doi-asserted-by":"publisher","first-page":"4257","DOI":"10.1109\/TIE.2017.2772190","volume":"65","author":"J Yang","year":"2018","unstructured":"Yang J, Zhou C, Yang S, Xu H, Hu B (2018) Anomaly detection based on zone partition for security protection of industrial cyber-physical systems. IEEE Trans Ind Electron 65(5):4257\u20134267","journal-title":"IEEE Trans Ind Electron"},{"issue":"11","key":"1507_CR31","doi-asserted-by":"publisher","first-page":"4766","DOI":"10.1109\/TII.2018.2804669","volume":"14","author":"H Wang","year":"2018","unstructured":"Wang H, Ruan J, Wang G, Zhou B, Liu Y, Fu X, Peng J (2018) Deep learning-based interval state estimation of AC smart grids against sparse cyber-attacks. IEEE Trans Ind Informat 14(11):4766\u20134778","journal-title":"IEEE Trans Ind Informat"},{"key":"1507_CR32","doi-asserted-by":"crossref","unstructured":"Yang H, Cheng L, Chuah MC (2019) Deep-learning-based network intrusion detection for SCADA systems. Proc IEEE Conf Commun Netw Secur. Washington, DC, USA, 337\u2013343","DOI":"10.1109\/CNS.2019.8802785"},{"key":"1507_CR33","doi-asserted-by":"publisher","first-page":"113578","DOI":"10.1016\/j.eswa.2020.113578","volume":"158","author":"J Liu","year":"2020","unstructured":"Liu J, Zhang W, Ma T, Tang Z, Xie Y, Gui W, Niyoyita JP (2020) Toward security monitoring of industrial cyber-physical systems via hierarchically distributed intrusion detection. Expert Syst Appl 158:113578\u2013113400","journal-title":"Expert Syst Appl"},{"issue":"3","key":"1507_CR34","doi-asserted-by":"publisher","first-page":"4627","DOI":"10.1109\/JIOT.2018.2871394","volume":"6","author":"C Qiu","year":"2019","unstructured":"Qiu C, Yu FR, Yao H, Jiang C, Xu F, Zhao C (2019) Blockchain-based software-defined industrial Internet of Things: A dueling deep Q -learning approach. IEEE Internet Things J 6(3):4627\u20134639","journal-title":"IEEE Internet Things J"},{"issue":"4","key":"1507_CR35","doi-asserted-by":"publisher","first-page":"3428","DOI":"10.1109\/TSG.2020.2973681","volume":"11","author":"M Ismail","year":"2020","unstructured":"Ismail M, Shaaban MF, Naidu M, Serpedin E (2020) Deep learning detection of electricity theft cyber-attacks in renewable distributed generation. IEEE Trans Smart Grid 11(4):3428\u20133437","journal-title":"IEEE Trans Smart Grid"},{"key":"1507_CR36","doi-asserted-by":"publisher","DOI":"10.1016\/j.ymssp.2019.106436","volume":"136","author":"J Arshad","year":"2020","unstructured":"Arshad J, Azad MA, Abdeltaif MM, Salah K (2020) An intrusion detection framework for energy constrained IoT devices. Mech Syst Signal Process 136:106436","journal-title":"Mech Syst Signal Process"},{"key":"1507_CR37","doi-asserted-by":"publisher","DOI":"10.1016\/j.cose.2021.102177","volume":"103","author":"Z Wang","year":"2021","unstructured":"Wang Z, Liu Y, He D, Chan S (2021) Intrusion detection methods based on integrated deep learning model. Comput Secur 103:102177","journal-title":"Comput Secur"},{"key":"1507_CR38","volume":"65","author":"AE Ibor","year":"2022","unstructured":"Ibor AE, Okunoye OB, Oladeji FA, Abdulsalam KA (2022) Novel hybrid model for intrusion prediction on cyber physical systems\u2019 communication networks based on bio-inspired deep neural network structure. J Inform Secur Appl 65:103107","journal-title":"J Inform Secur Appl"},{"key":"1507_CR39","doi-asserted-by":"crossref","unstructured":"Wickramasinghe S, Marino DL, Amarasinghe K, Manic M (2018) Generalization of deep learning for cyber-physical system security: A survey. Proc Ann Conf IEEE Ind Electr Soc 745\u2013751","DOI":"10.1109\/IECON.2018.8591773"},{"issue":"6","key":"1507_CR40","doi-asserted-by":"publisher","first-page":"1802","DOI":"10.1109\/JIOT.2017.2703172","volume":"4","author":"A Humayed","year":"2017","unstructured":"Humayed A, Lin J, Li F, Luo B (2017) Cyber-physical systems security-A survey. IEEE Internet Things J 4(6):1802\u20131831","journal-title":"IEEE Internet Things J"},{"key":"1507_CR41","doi-asserted-by":"crossref","unstructured":"Khan IA, Moustafa N, Pi D, Sallam KM, Zomaya AY, Li B (2021) A new explainable deep learning framework for cyber threat discovery in industrial IoT networks. IEEE Internet Things J","DOI":"10.1109\/JIOT.2021.3130156"},{"key":"1507_CR42","doi-asserted-by":"crossref","unstructured":"Wu C, Qian A, Dong X, Zhang Y (2020) Feature oriented design of visual analytics system for interpretable deep learning-based intrusion detection. Int Symp Theor Aspects Softw Eng (TASE) 73\u201380. IEEE","DOI":"10.1109\/TASE49443.2020.00019"},{"key":"1507_CR43","doi-asserted-by":"crossref","unstructured":"Burkart N, Franz M, Huber MF (2021) Explanation framework for intrusion detection. Mach Learn Cyber Phys Syst 83\u201391. Springer Vieweg, Berlin, Heidelberg","DOI":"10.1007\/978-3-662-62746-4_9"},{"key":"1507_CR44","doi-asserted-by":"crossref","unstructured":"Amarasinghe K, Kenney K, Manic M (2018) Toward explainable deep neural network-based anomaly detection. Int Conf Hum Syst Interact (HSI) 311\u2013317. IEEE","DOI":"10.1109\/HSI.2018.8430788"},{"key":"1507_CR45","doi-asserted-by":"crossref","unstructured":"Kauffmann J, M\u00fcller K-R, Montavon G (2020) Towards explaining anomalies: A deep taylor decomposition of one-class models. arXiv:1805.06230","DOI":"10.1016\/j.patcog.2020.107198"},{"key":"1507_CR46","doi-asserted-by":"crossref","unstructured":"Szczepanski,\u00a0Chora\u015b M,\u00a0Pawlicki M, Kozik R (2020) Achieving\u00a0explainability of intrusion detection system by hybrid oracle explainer approach. Int Joint Conf Neural Netw (IJCNN) 1\u20138. IEEE","DOI":"10.1109\/IJCNN48605.2020.9207199"},{"key":"1507_CR47","unstructured":"Pang G, Ding C, Shen C, Hengel AVD (2021) Explainable deep few-shot anomaly detection with deviation networks. arXiv preprint arXiv:2108.00462"},{"issue":"3","key":"1507_CR48","doi-asserted-by":"publisher","DOI":"10.1002\/cpe.5272","volume":"33","author":"S Krishnaveni","year":"2021","unstructured":"Krishnaveni S, Prabakaran S (2021) Ensemble approach for network threat detection and classification on cloud computing. Concurr Comput Pract Exp 33(3):e5272","journal-title":"Concurr Comput Pract Exp"},{"issue":"3","key":"1507_CR49","doi-asserted-by":"publisher","first-page":"783","DOI":"10.1007\/s13042-020-01202-7","volume":"12","author":"L Abualigah","year":"2021","unstructured":"Abualigah L, Alsalibi B, Shehab M, Alshinwan M, Khasawneh AM, Alabool H (2021) A parallel hybrid krill herd algorithm for feature selection. Int J Mach Learn Cybern 12(3):783\u2013806","journal-title":"Int J Mach Learn Cybern"},{"key":"1507_CR50","doi-asserted-by":"publisher","first-page":"4047","DOI":"10.1007\/s10489-018-1190-6","volume":"48","author":"LM Abualigah","year":"2018","unstructured":"Abualigah LM, Khader AT, Hanandeh ES (2018) Hybrid clustering analysis using improved krill herd algorithm. Appl Intell 48:4047\u20134071. https:\/\/doi.org\/10.1007\/s10489-018-1190-6","journal-title":"Appl Intell"},{"key":"1507_CR51","doi-asserted-by":"publisher","first-page":"16","DOI":"10.1016\/j.asoc.2018.11.047","volume":"76","author":"Q Tu","year":"2019","unstructured":"Tu Q, Chen X, Liu X (2019) Multi-strategy ensemble grey wolf optimizer and its application to feature selection. Appl Soft Comput 76:16\u201330","journal-title":"Appl Soft Comput"},{"issue":"12","key":"1507_CR52","doi-asserted-by":"publisher","first-page":"4446","DOI":"10.3390\/s22124446","volume":"22","author":"M Zulfiqar","year":"2022","unstructured":"Zulfiqar M, Gamage KA, Kamran M, Rasheed MB (2022) Hyperparameter optimization of bayesian neural network using bayesian optimization and intelligent feature engineering for load forecasting. Sensors 22(12):4446","journal-title":"Sensors"},{"key":"1507_CR53","doi-asserted-by":"publisher","DOI":"10.1016\/j.annals.2020.102925","volume":"83","author":"A Kulshrestha","year":"2020","unstructured":"Kulshrestha A, Krishnaswamy V, Sharma M (2020) Bayesian BILSTM approach for tourism demand forecasting. Ann Tour Res 83:102925","journal-title":"Ann Tour Res"},{"issue":"3","key":"1507_CR54","doi-asserted-by":"publisher","first-page":"1761","DOI":"10.1007\/s10586-020-03222-y","volume":"24","author":"S Krishnaveni","year":"2021","unstructured":"Krishnaveni S, Sivamohan S, Sridhar SS, Prabakaran S (2021) Efficient feature selection and classification through ensemble method for network intrusion detection on cloud computing. Clust Comput 24(3):1761\u20131779","journal-title":"Clust Comput"},{"issue":"3","key":"1507_CR55","doi-asserted-by":"publisher","first-page":"547","DOI":"10.1007\/s10207-021-00567-2","volume":"21","author":"M Soltani","year":"2022","unstructured":"Soltani M, Siavoshani MJ, Jahangir AH (2022) A content-based deep intrusion detection system. Int J Inf Secur 21(3):547\u2013562","journal-title":"Int J Inf Secur"},{"key":"1507_CR56","unstructured":"Mane S, Rao D (2021) Explaining network intrusion detection system using explainable AI framework.\u00a0arXiv preprint arXiv:2103.07110"},{"key":"1507_CR57","doi-asserted-by":"crossref","unstructured":"Eshmawi AA, Khayyat M, Abdel-Khalek S, Mansour RF, Dwivedi U, Joshi KK, Gupta D (2022) Deep learning with metaheuristics-based data sensing and encoding scheme for secure cyber physical sensor systems. Cluster Comput 1\u201313","DOI":"10.1007\/s10586-022-03654-8"},{"key":"1507_CR58","volume":"52","author":"AA Malibari","year":"2022","unstructured":"Malibari AA, Alotaibi SS, Alshahrani R, Dhahbi S, Alabdan R, Al-wesabi FN, Hilal AM (2022) A novel metaheuristic with deep learning enabled intrusion detection system for secured smart environment. Sustain Energy Technol Assess 52:102312","journal-title":"Sustain Energy Technol Assess"},{"issue":"1","key":"1507_CR59","doi-asserted-by":"publisher","first-page":"12937","DOI":"10.1038\/s41598-022-17043-z","volume":"12","author":"RF Mansour","year":"2022","unstructured":"Mansour RF (2022) Artificial intelligence-based optimization with deep learning model for blockchain enabled intrusion detection in CPS environment. Sci Rep 12(1):12937","journal-title":"Sci Rep"},{"issue":"5","key":"1507_CR60","doi-asserted-by":"publisher","first-page":"3081","DOI":"10.3390\/app13053081","volume":"13","author":"L Almuqren","year":"2023","unstructured":"Almuqren L, Maashi MS, Alamgeer M, Mohsen H, Hamza MA, Abdelmageed AA (2023) Explainable artificial intelligence enabled intrusion detection technique for secure cyber-physical systems. Appl Sci 13(5):3081","journal-title":"Appl Sci"},{"key":"1507_CR61","doi-asserted-by":"publisher","DOI":"10.1016\/j.measurement.2021.110145","volume":"186","author":"MM Althobaiti","year":"2021","unstructured":"Althobaiti MM, Kumar KPM, Gupta D, Kumar S, Mansour RF (2021) An intelligent cognitive computing-based intrusion detection for industrial cyber-physical systems. Measurement 186:110145","journal-title":"Measurement"},{"key":"1507_CR62","doi-asserted-by":"publisher","first-page":"7306","DOI":"10.1007\/s10489-021-02222-8","volume":"51","author":"IA Khan","year":"2021","unstructured":"Khan IA, Pi D, Khan N et al (2021) A privacy-conserving framework-based intrusion detection method for detecting and recognizing malicious behaviours in cyber-physical power networks. Appl Intell 51:7306\u20137321. https:\/\/doi.org\/10.1007\/s10489-021-02222-8","journal-title":"Appl Intell"},{"key":"1507_CR63","doi-asserted-by":"publisher","first-page":"181","DOI":"10.1016\/j.future.2021.09.010","volume":"127","author":"IA Khan","year":"2022","unstructured":"Khan IA, Moustafa N, Razzak I, Tanveer M, Pi D, Pan Y, Ali BS (2022) XSRU-IoMT: Explainable simple recurrent units for threat detection in Internet of Medical Things networks. Futur Gener Comput Syst 127:181\u2013193","journal-title":"Futur Gener Comput Syst"}],"container-title":["Peer-to-Peer Networking and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12083-023-01507-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s12083-023-01507-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12083-023-01507-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,7,12]],"date-time":"2023-07-12T04:21:30Z","timestamp":1689135690000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s12083-023-01507-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,6,27]]},"references-count":63,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2023,8]]}},"alternative-id":["1507"],"URL":"https:\/\/doi.org\/10.1007\/s12083-023-01507-8","relation":{},"ISSN":["1936-6442","1936-6450"],"issn-type":[{"value":"1936-6442","type":"print"},{"value":"1936-6450","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,6,27]]},"assertion":[{"value":"5 February 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 May 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 June 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval"}},{"value":"In terms of competing financial and non-financial interests, the authors declare no conflicts of interest.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}