{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,3]],"date-time":"2026-07-03T17:26:36Z","timestamp":1783099596039,"version":"3.54.6"},"reference-count":29,"publisher":"Springer Science and Business Media LLC","issue":"15","license":[{"start":{"date-parts":[[2023,3,10]],"date-time":"2023-03-10T00:00:00Z","timestamp":1678406400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,3,10]],"date-time":"2023-03-10T00:00:00Z","timestamp":1678406400000},"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":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2023,5]]},"DOI":"10.1007\/s00521-023-08319-0","type":"journal-article","created":{"date-parts":[[2023,3,10]],"date-time":"2023-03-10T13:02:53Z","timestamp":1678453373000},"page":"11459-11475","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":78,"title":["An optimized model for network intrusion detection systems in industry 4.0 using XAI based Bi-LSTM framework"],"prefix":"10.1007","volume":"35","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"}]}],"member":"297","published-online":{"date-parts":[[2023,3,10]]},"reference":[{"issue":"1","key":"8319_CR1","doi-asserted-by":"publisher","first-page":"546","DOI":"10.1109\/JSYST.2020.3023041","volume":"15","author":"B Bajic","year":"2020","unstructured":"Bajic B, Rikalovic A, Suzic N, Piuri V (2020) Industry 4.0 implementation challenges and opportunities: a managerial perspective. IEEE Syst J 15(1):546\u2013559","journal-title":"IEEE Syst J"},{"key":"8319_CR2","doi-asserted-by":"publisher","first-page":"113270","DOI":"10.1109\/ACCESS.2021.3103680","volume":"9","author":"TR Wanasinghe","year":"2021","unstructured":"Wanasinghe TR, Trinh T, Nguyen T, Gosine RG, James LA, Warrian PPJ (2021) Human centric digital transformation and operator 4.0 for the oil and gas industry. IEEE Access 9:113270\u2013113291","journal-title":"IEEE Access"},{"issue":"11","key":"8319_CR3","doi-asserted-by":"publisher","first-page":"1257","DOI":"10.3390\/electronics10111257","volume":"10","author":"MA Ferrag","year":"2021","unstructured":"Ferrag MA, Shu L, Djallel H, Choo KKR (2021) Deep learning-based intrusion detection for distributed denial of service attack in agriculture 4.0. Electronics 10(11):1257","journal-title":"Electronics"},{"issue":"6","key":"8319_CR4","doi-asserted-by":"publisher","first-page":"1922","DOI":"10.1080\/00207543.2020.1824085","volume":"59","author":"T Zheng","year":"2021","unstructured":"Zheng T, Ardolino M, Bacchetti A, Perona M (2021) The applications of Industry 4.0 technologies in manufacturing context: a systematic literature review. Int J Prod Res 59(6):1922\u20131954","journal-title":"Int J Prod Res"},{"key":"8319_CR5","doi-asserted-by":"crossref","unstructured":"Kiran MB (2021) Significance of intruder detection techniques in the context of industry 4.0. In: Proceedings of the international conference on industrial engineering and operations management. pp 2977\u20132985","DOI":"10.46254\/SA02.20210835"},{"key":"8319_CR6","doi-asserted-by":"publisher","first-page":"107094","DOI":"10.1016\/j.comnet.2019.107094","volume":"169","author":"MZ Gunduz","year":"2020","unstructured":"Gunduz MZ, Das R (2020) Cyber-security on smart grid: Threats and potential solutions. Comput Netw 169:107094","journal-title":"Comput Netw"},{"key":"8319_CR7","doi-asserted-by":"crossref","unstructured":"Ahmad I, Shah SAA, Al-Khasawneh MA (2021) Performance Analysis of Intrusion Detection systems for smartphone security enhancements. In: 2021 2nd international conference on smart computing and electronic enterprise (ICSCEE), pp 19\u201325, IEEE","DOI":"10.1109\/ICSCEE50312.2021.9497904"},{"key":"8319_CR8","doi-asserted-by":"publisher","first-page":"122794","DOI":"10.1016\/j.jclepro.2020.122794","volume":"274","author":"M Sun","year":"2020","unstructured":"Sun M, Li X, Yang R, Zhang Y, Zhang L, Song Z, Liu Q, Zhao D (2020) Comprehensive partitions and different strategies based on ecological security and economic development in Guizhou Province. China J Clean Prod 274:122794","journal-title":"China J Clean Prod"},{"issue":"4","key":"8319_CR9","doi-asserted-by":"publisher","first-page":"602","DOI":"10.3390\/electronics11040602","volume":"11","author":"FB Saghezchi","year":"2022","unstructured":"Saghezchi FB, Mantas G, Violas MA, de Oliveira Duarte AM, Rodriguez J (2022) Machine learning for DDoS attack detection in industry 4.0 CPPSs. Electronics 11(4):602","journal-title":"Electronics"},{"issue":"9","key":"8319_CR10","doi-asserted-by":"publisher","first-page":"1460","DOI":"10.3390\/electronics9091460","volume":"9","author":"N Saxena","year":"2020","unstructured":"Saxena N, Hayes E, Bertino E, Ojo PP, Choo KKR, Burnap PP (2020) Impact and key challenges of insider threats on organizations and critical businesses. Electronics 9(9):1460","journal-title":"Electronics"},{"key":"8319_CR11","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s11571-022-09780-8","volume":"16","author":"MA Alohali","year":"2022","unstructured":"Alohali MA, Al-Wesabi FN, Hilal AM, Goel S, Gupta D, Khanna A (2022) Artificial intelligence enabled intrusion detection systems for cognitive cyber-physical systems in industry 4.0 environment. Cognit Neurodyn 16:1\u201313","journal-title":"Cognit Neurodyn"},{"key":"8319_CR12","doi-asserted-by":"publisher","first-page":"6398","DOI":"10.1109\/TII.2021.3133384","volume":"18","author":"B Tahir","year":"2021","unstructured":"Tahir B, Jolfaei A, Tariq M (2021) Experience driven attack design and federated learning based intrusion detection in industry 4.0. IEEE Trans Ind Inf 18:6398\u20136405","journal-title":"IEEE Trans Ind Inf"},{"key":"8319_CR13","doi-asserted-by":"publisher","first-page":"6503","DOI":"10.1109\/TII.2021.3139363","volume":"18","author":"L Qi","year":"2021","unstructured":"Qi L, Yang Y, Zhou X, Rafique W, Ma J (2021) Fast anomaly identification based on multi-aspect data streams for intelligent intrusion detection toward secure industry 4.0. IEEE Trans Ind Inf 18:6503\u20136511","journal-title":"IEEE Trans Ind Inf"},{"key":"8319_CR14","doi-asserted-by":"publisher","first-page":"251","DOI":"10.1109\/TII.2022.3157727","volume":"19","author":"K Yang","year":"2022","unstructured":"Yang K, Shi Y, Yu Z, Yang Q, Sangaiah AK, Zeng H (2022) Stacked one-class broad learning system for intrusion detection in industry 4.0. IEEE Trans Ind Inf 19:251\u2013260","journal-title":"IEEE Trans Ind Inf"},{"key":"8319_CR15","doi-asserted-by":"crossref","unstructured":"Ibitoye O, Shafiq O, Matrawy A (2019) Analyzing adversarial attacks against deep learning for intrusion detection in IoT networks. In: 2019 IEEE global communications conference (GLOBECOM), pp. 1\u20136. IEEE","DOI":"10.1109\/GLOBECOM38437.2019.9014337"},{"issue":"4","key":"8319_CR16","doi-asserted-by":"publisher","first-page":"602","DOI":"10.3390\/electronics11040602","volume":"11","author":"FB Saghezchi","year":"2022","unstructured":"Saghezchi FB, Mantas G, Violas MA, de Oliveira Duarte AM, Rodriguez J (2022) Machine learning for DDoS attack detection in industry 4.0 CPPSs. Electronics 11(4):602","journal-title":"Electronics"},{"key":"8319_CR17","doi-asserted-by":"crossref","unstructured":"Khoa TV, Saputra YM, Hoang DT, Trung NL, Nguyen D, Ha NV, Dutkiewicz E (2020) Collaborative learning model for cyberattack detection systems in iot industry 4.0. In: 2020 IEEE wireless communications and networking conference WCNC,\u00a0pp. 1\u20136. IEEE.","DOI":"10.1109\/WCNC45663.2020.9120761"},{"issue":"8","key":"8319_CR18","doi-asserted-by":"publisher","first-page":"5615","DOI":"10.1109\/TII.2020.3023430","volume":"17","author":"B Li","year":"2020","unstructured":"Li B, Wu Y, Song J, Lu R, Li T, Zhao L (2020) DeepFed: Federated deep learning for intrusion detection in industrial cyber\u2013physical systems. IEEE Trans Industr Inf 17(8):5615\u20135624","journal-title":"IEEE Trans Industr Inf"},{"key":"8319_CR19","doi-asserted-by":"publisher","DOI":"10.1002\/itl2.381pp.e381","author":"D Chowdhury","year":"2022","unstructured":"Chowdhury D, Poddar S, Banarjee S, Pal R, Gani A, Ellis C, Arya RC, Gill SS, Uhlig S (2022) CovidXAI: explainable ai-assisted web application for COVID-19 vaccine prioritisation. Int Technol Lett. https:\/\/doi.org\/10.1002\/itl2.381pp.e381","journal-title":"Int Technol Lett"},{"issue":"11","key":"8319_CR20","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"},{"issue":"3","key":"8319_CR21","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":"8319_CR22","doi-asserted-by":"publisher","first-page":"167","DOI":"10.1109\/LNET.2022.3186589","volume":"4","author":"PP Barnard","year":"2022","unstructured":"Barnard PP, Marchetti N, DaSilva LA (2022) Robust network intrusion detection through explainable artificial intelligence (XAI). IEEE Netw Lett 4(3):167\u2013171","journal-title":"IEEE Netw Lett"},{"issue":"4","key":"8319_CR23","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10922-021-09606-8","volume":"29","author":"H Liu","year":"2021","unstructured":"Liu H, Zhong C, Alnusair A, Islam SR (2021) FAIXID: a framework for enhancing ai explainability of intrusion detection results using data cleaning techniques. J Netw Syst Manage 29(4):1\u201330","journal-title":"J Netw Syst Manage"},{"issue":"2","key":"8319_CR24","doi-asserted-by":"publisher","first-page":"656","DOI":"10.3390\/s21020656","volume":"21","author":"X Larriva-Novo","year":"2021","unstructured":"Larriva-Novo X, Villagr\u00e1 VA, Vega-Barbas M, Rivera D, Sanz Rodrigo M (2021) An IoT-focused intrusion detection system approach based on preprocessing characterization for cybersecurity datasets. Sensors 21(2):656","journal-title":"Sensors"},{"key":"8319_CR25","doi-asserted-by":"publisher","DOI":"10.1155\/2021\/8830431","author":"X Li","year":"2021","unstructured":"Li X, Yi PP, Wei W, Jiang Y, Tian L (2021) LNNLS-KH: a feature selection method for network intrusion detection. Sec Commun Netw. https:\/\/doi.org\/10.1155\/2021\/8830431","journal-title":"Sec Commun Netw"},{"issue":"5","key":"8319_CR26","doi-asserted-by":"crossref","first-page":"528","DOI":"10.1016\/j.jksuci.2018.04.007","volume":"33","author":"KB Resma","year":"2021","unstructured":"Resma KB, Nair MS (2021) Multilevel thresholding for image segmentation using Krill Herd optimization algorithm. J King Saud Univ-Comput Inf Sci 33(5):528\u2013541","journal-title":"J King Saud Univ-Comput Inf Sci"},{"key":"8319_CR27","doi-asserted-by":"publisher","first-page":"107395","DOI":"10.1016\/j.compeleceng.2021.107395","volume":"95","author":"W Abdul","year":"2021","unstructured":"Abdul W, Alsulaiman M, Amin SU, Faisal M, Muhammad G, Albogamy FR, Bencherif MA, Ghaleb H (2021) Intelligent real-time Arabic sign language classification using attention-based inception and BiLSTM. Comput Electr Eng 95:107395","journal-title":"Comput Electr Eng"},{"issue":"3","key":"8319_CR28","doi-asserted-by":"publisher","first-page":"740","DOI":"10.3390\/make3030037","volume":"3","author":"S Knapi\u010d","year":"2021","unstructured":"Knapi\u010d S, Malhi A, Saluja R, Fr\u00e4mling K (2021) Explainable artificial intelligence for human decision support system in the medical domain. Mach Learn Knowl Extractio 3(3):740\u2013770","journal-title":"Mach Learn Knowl Extractio"},{"key":"8319_CR29","doi-asserted-by":"crossref","unstructured":"Kwon D, Natarajan K, Suh SC, Kim H, Kim J (2018) An empirical study on network anomaly detection using convolutional neural networks. In: ICDCS pp 1595\u20131598","DOI":"10.1109\/ICDCS.2018.00178"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-023-08319-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-023-08319-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-023-08319-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,9]],"date-time":"2025-04-09T15:28:25Z","timestamp":1744212505000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-023-08319-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,3,10]]},"references-count":29,"journal-issue":{"issue":"15","published-print":{"date-parts":[[2023,5]]}},"alternative-id":["8319"],"URL":"https:\/\/doi.org\/10.1007\/s00521-023-08319-0","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,3,10]]},"assertion":[{"value":"23 May 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 January 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 March 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":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to participate"}},{"value":"Not applicable.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"This article does not contain any studies with human or animal subjects performed by any of the authors.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Human and animal rights"}},{"value":"For this type of study, informed consent is not required.","order":6,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed consent"}}]}}