{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,13]],"date-time":"2026-05-13T08:12:14Z","timestamp":1778659934771,"version":"3.51.4"},"reference-count":55,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,4,2]],"date-time":"2026-04-02T00:00:00Z","timestamp":1775088000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2026,5,13]],"date-time":"2026-05-13T00:00:00Z","timestamp":1778630400000},"content-version":"vor","delay-in-days":41,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"name":"Institute of Information and Communications Technology Planning and Evaluation","award":["RS-2024-00354169"],"award-info":[{"award-number":["RS-2024-00354169"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Appl Netw Sci"],"DOI":"10.1007\/s41109-026-00793-4","type":"journal-article","created":{"date-parts":[[2026,4,2]],"date-time":"2026-04-02T18:35:44Z","timestamp":1775154944000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["The impact of network preprocessing structure design on XAI explanation quality"],"prefix":"10.1007","volume":"11","author":[{"given":"Jinhyuk","family":"Son","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Homook","family":"Cho","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jaewan","family":"Hong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yongho","family":"Kim","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,4,2]]},"reference":[{"key":"793_CR1","unstructured":"Adebayo J, Gilmer J, Muelly M, Goodfellow I, Hardt M, Kim B (2018) Sanity checks for saliency maps. In: Bengio S, Wallach H, Larochelle H, Grauman K, Cesa-Bianchi N, Garnett R (eds) Advances in neural information processing systems, vol 31. Curran Associates, Inc. https:\/\/proceedings.neurips.cc\/paper_files\/paper\/2018\/file\/294a8ed24b1ad22ec2e7efea049b8737-Paper.pdf"},{"key":"793_CR2","unstructured":"Ancona M, Ceolini E, \u00d6ztireli C, Gross MH (2017)Towards better understanding of gradient-based attribution methods for deep neural networks. In: International conference on learning representations. https:\/\/api.semanticscholar.org\/CorpusID:3728967"},{"key":"793_CR3","doi-asserted-by":"publisher","unstructured":"Arshad S, Zanib R, Akram A, Saeed T (2023) A short review on faster and more reliable tcp reassembly for high-speed networks in deep packet inspection. In: 2023 1st international conference on advanced innovations in smart cities (ICAISC), 1\u20136 . https:\/\/doi.org\/10.1109\/ICAISC56366.2023.10085644","DOI":"10.1109\/ICAISC56366.2023.10085644"},{"key":"793_CR4","doi-asserted-by":"publisher","unstructured":"AsSadhan B, Moura JMF, Lapsley D, Jones C, Strayer WT (2009) Detecting botnets using command and control traffic. In: 2009 eighth IEEE international symposium on network computing and applications, 156\u2013162. https:\/\/doi.org\/10.1109\/NCA.2009.56","DOI":"10.1109\/NCA.2009.56"},{"key":"793_CR5","doi-asserted-by":"publisher","unstructured":"Barredo Arrieta A, D\u00edaz-Rodr\u00edguez N, Del Ser J, Bennetot A, Tabik S, Barbado A, Garcia S, Gil-Lopez S, Molina D, Benjamins R, Chatila R, Herrera F (2020) Explainable artificial intelligence (xai): Concepts, taxonomies, opportunities and challenges toward responsible ai. Inf Fus 58, 82\u2013115 https:\/\/doi.org\/10.1016\/j.inffus.2019.12.012","DOI":"10.1016\/j.inffus.2019.12.012"},{"key":"793_CR6","doi-asserted-by":"publisher","unstructured":"Bhatt U, Xiang A, Sharma S, Weller A, Taly A, Jia Y, Ghosh J, Puri R, Moura JMF, Eckersley P (2020) Explainable machine learning in deployment. In: Proceedings of the 2020 conference on fairness, accountability, and transparency. FAT*\u201920, pp 648\u2013657. Association for computing machinery, New York. https:\/\/doi.org\/10.1145\/3351095.3375624","DOI":"10.1145\/3351095.3375624"},{"issue":"2","key":"793_CR7","doi-asserted-by":"publisher","first-page":"1153","DOI":"10.1109\/COMST.2015.2494502","volume":"18","author":"AL Buczak","year":"2015","unstructured":"Buczak AL, Guven E (2015) A survey of data mining and machine learning methods for cyber security intrusion detection. IEEE Commun Surv Tutor 18(2):1153\u20131176","journal-title":"IEEE Commun Surv Tutor"},{"key":"793_CR8","doi-asserted-by":"publisher","unstructured":"Carvalho DV, Pereira EM, Cardoso JS (2019) Machine learning interpretability: a survey on methods and metrics. Electronics. https:\/\/doi.org\/10.3390\/electronics8080832","DOI":"10.3390\/electronics8080832"},{"issue":"13","key":"793_CR9","doi-asserted-by":"publisher","first-page":"4166","DOI":"10.3390\/s25134166","volume":"25","author":"Z Cheng","year":"2025","unstructured":"Cheng Z, Wu Y, Li Y, Cai L, Ihnaini B (2025) A comprehensive review of explainable artificial intelligence (xai) in computer vision. Sensors 25(13):4166","journal-title":"Sensors"},{"key":"793_CR10","unstructured":"Chen J, Song L, Wainwright M, Jordan M (2018) Learning to explain: an information-theoretic perspective on model interpretation. In: Dy J, Krause A (eds) Proceedings of the 35th international conference on machine learning. Proceedings of machine learning research, vol 80, pp 883\u2013892. PMLR. https:\/\/proceedings.mlr.press\/v80\/chen18j.html"},{"key":"793_CR11","doi-asserted-by":"publisher","first-page":"401","DOI":"10.1007\/978-3-031-20319-0_30","volume-title":"Advanced research in technologies, information, innovation and sustainability","author":"L Coroama","year":"2022","unstructured":"Coroama L, Groza A (2022) Evaluation metrics in explainable artificial intelligence (xai). In: Guarda T, Portela F, Augusto MF (eds) Advanced research in technologies, information, innovation and sustainability. Springer, Cham, pp 401\u2013413"},{"key":"793_CR12","doi-asserted-by":"publisher","unstructured":"Devaraju S, Ramakrishnan S, Jawahar S, Soni D, Somasundaram A (2022) Entropy-based feature selection for network intrusion detection systems. In: Editor1, Editor2 (eds) Methods, implementation, and application of cyber security intelligence and analytics, pp. 201\u2013225. IGI Global. https:\/\/doi.org\/10.4018\/978-1-6684-3991-3.ch012","DOI":"10.4018\/978-1-6684-3991-3.ch012"},{"key":"793_CR13","doi-asserted-by":"publisher","unstructured":"DeYoung J, Jain S, Rajani NF, Lehman E, Xiong C, Socher R, Wallace BC (2020) ERASER: A benchmark to evaluate rationalized NLP models. In: Jurafsky D, Chai J, Schluter N, Tetreault J (eds) Proceedings of the 58th annual meeting of the association for computational linguistics, pp 4443\u20134458. Association for computational linguistics. https:\/\/doi.org\/10.18653\/v1\/2020.acl-main.408","DOI":"10.18653\/v1\/2020.acl-main.408"},{"key":"793_CR14","doi-asserted-by":"crossref","unstructured":"Ding Y, Zhai Y (2018) Intrusion detection system for NSL-KDD dataset using convolutional neural networks. In: Proceedings of the 2018 2nd international conference on computer science and artificial intelligence, 81\u201385","DOI":"10.1145\/3297156.3297230"},{"key":"793_CR15","unstructured":"Doshi-Velez F, Kim B (2017) Towards a rigorous science of interpretable machine learning. arXiv: Machine Learning"},{"key":"793_CR16","doi-asserted-by":"publisher","unstructured":"Do\u0161ilovi\u0107 FK, Br\u010di\u0107 M, Hlupi\u0107 N (2018) Explainable artificial intelligence: a survey. In: 2018 41st international convention on information and communication technology, electronics and microelectronics (MIPRO), pp 0210\u20130215 . https:\/\/doi.org\/10.23919\/MIPRO.2018.8400040","DOI":"10.23919\/MIPRO.2018.8400040"},{"key":"793_CR17","volume":"50","author":"MA Ferrag","year":"2020","unstructured":"Ferrag MA, Maglaras L, Moschoyiannis S, Janicke H (2020) Deep learning for cyber security intrusion detection: approaches, datasets, and comparative study. J Inf Secur Appl 50:102419","journal-title":"J Inf Secur Appl"},{"key":"793_CR18","doi-asserted-by":"crossref","unstructured":"Fuster GG, Jasmontaite L (2020) Cybersecurity regulation in the European union: the digital, the critical and fundamental rights. In: The ethics of cybersecurity (pp. 97-115). Cham: Springer International Publishing","DOI":"10.1007\/978-3-030-29053-5_5"},{"key":"793_CR19","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1016\/j.cose.2008.08.003","volume":"28","author":"P Garcia-Teodoro","year":"2009","unstructured":"Garcia-Teodoro P, Diaz-Verdejo J, Maci\u00e1-Fern\u00e1ndez G, V\u00e1zquez E (2009) Anomaly-based network intrusion detection: techniques, systems and challenges. Comput Secur 28:18\u201328","journal-title":"Comput Secur"},{"issue":"9","key":"793_CR20","doi-asserted-by":"publisher","first-page":"1263","DOI":"10.1109\/TKDE.2008.239","volume":"21","author":"H He","year":"2009","unstructured":"He H, Garcia EA (2009) Learning from imbalanced data. IEEE Trans Knowl Data Eng 21(9):1263\u20131284. https:\/\/doi.org\/10.1109\/TKDE.2008.239","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"793_CR21","doi-asserted-by":"publisher","first-page":"104650","DOI":"10.1109\/ACCESS.2020.3000179","volume":"8","author":"H Hindy","year":"2020","unstructured":"Hindy H, Brosset D, Bayne E, Seeam AK, Tachtatzis C, Atkinson R, Bellekens X (2020) A taxonomy of network threats and the effect of current datasets on intrusion detection systems. IEEE Access 8:104650\u2013104675. https:\/\/doi.org\/10.1109\/ACCESS.2020.3000179","journal-title":"IEEE Access"},{"issue":"4","key":"793_CR22","doi-asserted-by":"publisher","first-page":"1312","DOI":"10.1002\/widm.1312","volume":"9","author":"A Holzinger","year":"2019","unstructured":"Holzinger A, Langs G, Denk H, Zatloukal K, M\u00fcller H (2019) Causability and explainability of artificial intelligence in medicine. Wiley interdisciplinary reviews data mining and knowledge discovery 9(4):1312","journal-title":"Wiley interdisciplinary reviews data mining and knowledge discovery"},{"key":"793_CR23","doi-asserted-by":"publisher","unstructured":"Holzinger A, Biemann C, Pattichis C, Kell D (2017) What do we need to build explainable ai systems for the medical domain? https:\/\/doi.org\/10.48550\/arXiv.1712.09923","DOI":"10.48550\/arXiv.1712.09923"},{"key":"793_CR24","unstructured":"Hooker S, Erhan D, Kindermans P-J, Kim B (2019) A benchmark for interpretability methods in deep neural networks. In: Advances in neural information processing systems, vol. 32. Curran Associates, Inc"},{"key":"793_CR25","unstructured":"Jain S, Wallace BC (2019) Attention is not eplanation. https:\/\/arxiv.org\/abs\/1902.10186"},{"issue":"5","key":"793_CR26","doi-asserted-by":"publisher","first-page":"429","DOI":"10.3233\/IDA-2002-6504","volume":"6","author":"N Japkowicz","year":"2002","unstructured":"Japkowicz N, Stephen S (2002) The class imbalance problem: a systematic study1. Intell Data Anal 6(5):429\u2013449. https:\/\/doi.org\/10.3233\/IDA-2002-6504","journal-title":"Intell Data Anal"},{"key":"793_CR27","unstructured":"Lundberg SM, Lee S-I (2017) A unified approach to interpreting model predictions. In: Guyon I, Luxburg UV, Bengio S, Wallach H, Fergus R, Vishwanathan S, Garnett R (eds) Advances in neural information processing systems, vol. 30. Curran Associates, Inc"},{"key":"793_CR28","doi-asserted-by":"publisher","unstructured":"Miller T (2019) Explanation in artificial intelligence: Insights from the social sciences. Artificial Intelligence 267, 1\u201338 https:\/\/doi.org\/10.1016\/j.artint.2018.07.007","DOI":"10.1016\/j.artint.2018.07.007"},{"issue":"5","key":"793_CR29","doi-asserted-by":"publisher","first-page":"3503","DOI":"10.1007\/s10462-021-10088-y","volume":"55","author":"D Minh","year":"2022","unstructured":"Minh D, Wang HX, Li YF, Nguyen TN (2022) Explainable artificial intelligence: a comprehensive review. Artif Intell Rev 55(5):3503\u20133568","journal-title":"Artif Intell Rev"},{"issue":"16","key":"793_CR30","doi-asserted-by":"publisher","first-page":"8162","DOI":"10.3390\/app12168162","volume":"12","author":"L Mohammadpour","year":"2022","unstructured":"Mohammadpour L, Ling TC, Liew CS, Aryanfar A (2022) A survey of CNN-based network intrusion detection. Appl Sci 12(16):8162","journal-title":"Appl Sci"},{"key":"793_CR31","doi-asserted-by":"publisher","unstructured":"Moustafa N, Slay J (2015) Unsw-nb15: a comprehensive data set for network intrusion detection systems (unsw-nb15 network data set). In: 2015 military communications and information systems conference (MilCIS), pp 1\u20136. https:\/\/doi.org\/10.1109\/MilCIS.2015.7348942","DOI":"10.1109\/MilCIS.2015.7348942"},{"issue":"1","key":"793_CR32","doi-asserted-by":"publisher","first-page":"1000","DOI":"10.1109\/TITS.2022.3188671","volume":"24","author":"A Oseni","year":"2023","unstructured":"Oseni A, Moustafa N, Creech G, Sohrabi N, Strelzoff A, Tari Z, Linkov I (2023) An explainable deep learning framework for resilient intrusion detection in IoT-enabled transportation networks. IEEE Trans Intell Transp Syst 24(1):1000\u20131014. https:\/\/doi.org\/10.1109\/TITS.2022.3188671","journal-title":"IEEE Trans Intell Transp Syst"},{"key":"793_CR33","doi-asserted-by":"publisher","unstructured":"Patil S, Varadarajan V, Mazhar SM, Sahibzada A, Ahmed N, Sinha O, Kumar S, Shaw K, Kotecha K (2022) Explainable artificial intelligence for intrusion detection system. Electronics. https:\/\/doi.org\/10.3390\/electronics11193079","DOI":"10.3390\/electronics11193079"},{"key":"793_CR34","doi-asserted-by":"publisher","first-page":"12345","DOI":"10.1109\/ACCESS.2025.3555861","volume":"13","author":"P Phalaagae","year":"2025","unstructured":"Phalaagae P, Zungeru AM, Yahya A, Sigweni B, Rajalakshmi S (2025) A hybrid CNN-LSTM model with attention mechanism for improved intrusion detection in wireless IoT sensor networks. IEEE Access 13:12345\u201312359","journal-title":"IEEE Access"},{"key":"793_CR35","doi-asserted-by":"publisher","unstructured":"Ponomarev S, Atkison T (2016) Session duration based feature extraction for network intrusion detection in control system networks. In: 2016 International conference on computational science and computational intelligence (CSCI), pp 892\u2013896 . https:\/\/doi.org\/10.1109\/CSCI.2016.0173","DOI":"10.1109\/CSCI.2016.0173"},{"issue":"8","key":"793_CR36","doi-asserted-by":"publisher","first-page":"4921","DOI":"10.3390\/app13084921","volume":"13","author":"EUH Qazi","year":"2023","unstructured":"Qazi EUH, Faheem MH, Zia T (2023) Hdlnids: hybrid deep-learning-based network intrusion detection system. Appl Sci 13(8):4921","journal-title":"Appl Sci"},{"key":"793_CR37","unstructured":"Radford A, Kim JW, Hallacy C, Ramesh A, Goh G, Agarwal S, Sastry G, Askell A, Mishkin P, Clark J, Krueger G, Sutskever I (2021) Learning transferable visual models from natural language supervision. In: Meila M, Zhang T (eds) Proceedings of the 38th international conference on machine learning. Proceedings of machine learning research, vol 139, pp 8748\u20138763. PMLR. https:\/\/proceedings.mlr.press\/v139\/radford21a.html"},{"key":"793_CR38","doi-asserted-by":"publisher","unstructured":"Ribeiro MT, Singh S, Guestrin C (2016) why should i trust you?: explaining the predictions of any classifier. In: Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining. KDD \u201916, pp 1135\u20131144. Association for computing machinery, New York . https:\/\/doi.org\/10.1145\/2939672.2939778","DOI":"10.1145\/2939672.2939778"},{"key":"793_CR39","doi-asserted-by":"publisher","first-page":"147","DOI":"10.1016\/j.cose.2019.06.005","volume":"86","author":"M Ring","year":"2019","unstructured":"Ring M, Wunderlich S, Scheuring D, Landes D, Hotho A (2019) A survey of network-based intrusion detection data sets. Comput Secur 86:147\u2013167. https:\/\/doi.org\/10.1016\/j.cose.2019.06.005","journal-title":"Comput Secur"},{"key":"793_CR40","doi-asserted-by":"publisher","first-page":"156","DOI":"10.1016\/j.cose.2018.12.012","volume":"82","author":"M Ring","year":"2019","unstructured":"Ring M, Schl\u00f6r D, Landes D, Hotho A (2019) Flow-based network traffic generation using generative adversarial networks. Comput Secur 82:156\u2013172","journal-title":"Comput Secur"},{"issue":"3","key":"793_CR41","doi-asserted-by":"publisher","first-page":"247","DOI":"10.1109\/JPROC.2021.3060483","volume":"109","author":"W Samek","year":"2021","unstructured":"Samek W, Montavon G, Lapuschkin S, Anders CJ, M\u00fcller K-R (2021) Explaining deep neural networks and beyond: a review of methods and applications. Proc IEEE 109(3):247\u2013278. https:\/\/doi.org\/10.1109\/JPROC.2021.3060483","journal-title":"Proc IEEE"},{"key":"793_CR42","doi-asserted-by":"publisher","unstructured":"Satheesh N, Rathnamma MV, Rajeshkumar G, Sagar PV, Dadheech P, Dogiwal SR, Velayutham P, Sengan S (2020) Flow-based anomaly intrusion detection using machine learning model with software defined networking for openflow network. Microprocessors Microsystems 79, 103285 https:\/\/doi.org\/10.1016\/j.micpro.2020.103285","DOI":"10.1016\/j.micpro.2020.103285"},{"key":"793_CR43","doi-asserted-by":"publisher","unstructured":"Serrano S, Smith NA (2019) Is attention interpretable? In: Korhonen A, Traum D, M\u00e0rquez L (eds) Proceedings of the 57th annual meeting of the association for computational linguistics, pp 2931\u20132951. Association for computational linguistics, Florence. https:\/\/doi.org\/10.18653\/v1\/P19-1282","DOI":"10.18653\/v1\/P19-1282"},{"issue":"1","key":"793_CR44","doi-asserted-by":"publisher","first-page":"69","DOI":"10.3390\/electronics14010069","volume":"14","author":"V Shanmugam","year":"2025","unstructured":"Shanmugam V, Razavi-Far B, Hallaji E (2025) Addressing class imbalance in intrusion detection: a comprehensive evaluation of machine learning approaches. Electronics 14(1):69. https:\/\/doi.org\/10.3390\/electronics14010069","journal-title":"Electronics"},{"issue":"2018","key":"793_CR45","first-page":"108","volume":"1","author":"I Sharafaldin","year":"2018","unstructured":"Sharafaldin I, Lashkari AH, Ghorbani AA (2018) Toward generating a new intrusion detection dataset and intrusion traffic characterization. ICISSp 1(2018):108\u2013116","journal-title":"ICISSp"},{"key":"793_CR46","doi-asserted-by":"crossref","unstructured":"Sithakoul S, Meftah S, Feutry C (2024) Beexai: Benchmark to evaluate explainable ai. In: Proceedings of the world conference on explainable artificial intelligence, pp. 445\u2013468. Springer, Cham","DOI":"10.1007\/978-3-031-63787-2_23"},{"key":"793_CR47","unstructured":"Stanchi OA, Ronchetti F, Dal\u00a0Bianco PA, R\u00edos GG, Hasperu\u00e9 W, Puig\u00a0Valls D, Quiroga FM (2024) Quantitative evaluation of white & black box interpretability methods for image classification. In: XXX Congreso Argentino de Ciencias de la Computaci\u00f3n (CACIC), La Plata, Argentina, pp 125\u2013134"},{"key":"793_CR48","unstructured":"Sundararajan M, Taly A, Yan Q (2017) Axiomatic attribution for deep networks. In: Precup D, Teh YW (eds) Proceedings of the 34th international conference on machine learning. Proceedings of machine learning research, vol 70, pp 3319\u20133328. PMLR. https:\/\/proceedings.mlr.press\/v70\/sundararajan17a.html"},{"key":"793_CR49","doi-asserted-by":"crossref","unstructured":"Tavallaee M, Bagheri E, Lu W, Ghorbani AA (2009) A detailed analysis of the KDD CUP 99 data set. In: 2009 IEEE symposium on computational intelligence for security and defense applications, 1\u20136","DOI":"10.1109\/CISDA.2009.5356528"},{"key":"793_CR50","doi-asserted-by":"publisher","unstructured":"Ugurlu M, Dogru IA (2019) A survey on deep learning based intrusion detection system. In: 2019 4th international conference on computer science and engineering (UBMK), pp. 223\u2013228. https:\/\/doi.org\/10.1109\/UBMK.2019.8907206","DOI":"10.1109\/UBMK.2019.8907206"},{"key":"793_CR51","doi-asserted-by":"publisher","unstructured":"Vubangsi M, Mangai TR, Olukayode A, Mubarak AS, Al-Turjman F (2024) 13 - bert-ids: an intrusion detection system based on bidirectional encoder representations from transformers. In: Al-Turjman F (ed) Computational intelligence and blockchain in complex systems. Advanced studies in complex systems, pp 147\u2013155. Morgan Kaufman. https:\/\/doi.org\/10.1016\/B978-0-443-13268-1.00021-2","DOI":"10.1016\/B978-0-443-13268-1.00021-2"},{"key":"793_CR52","doi-asserted-by":"publisher","DOI":"10.1186\/s13635-025-00191-w","author":"Y Yang","year":"2025","unstructured":"Yang Y, Peng X (2025). Bert-based network for intrusion detection system. https:\/\/doi.org\/10.1186\/s13635-025-00191-w","journal-title":"Bert-based network for intrusion detection system"},{"key":"793_CR53","doi-asserted-by":"crossref","unstructured":"Yang H, Cheng L, Chuah MC (2019) Deep-learning-based network intrusion detection for SCADA systems. In 2019 IEEE Conference on Communications and Network Security (CNS), 1\u20137","DOI":"10.1109\/CNS.2019.8802785"},{"key":"793_CR54","doi-asserted-by":"crossref","unstructured":"Yang H, Cheng L, Chuah MC (2019) Deep-learning-based network intrusion detection for SCADA systems. In: 2019 IEEE conference on communications and network security (CNS), 1\u20137","DOI":"10.1109\/CNS.2019.8802785"},{"key":"793_CR55","doi-asserted-by":"publisher","first-page":"93104","DOI":"10.1109\/ACCESS.2022.3204051","volume":"10","author":"Z Zhang","year":"2022","unstructured":"Zhang Z, Hamadi HA, Damiani E, Yeun CY, Taher F (2022) Explainable artificial intelligence applications in cyber security: state-of-the-art in research. IEEE Access 10:93104\u201393139. https:\/\/doi.org\/10.1109\/ACCESS.2022.3204051","journal-title":"IEEE Access"}],"container-title":["Applied Network Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s41109-026-00793-4","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s41109-026-00793-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s41109-026-00793-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,13]],"date-time":"2026-05-13T07:45:20Z","timestamp":1778658320000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s41109-026-00793-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4,2]]},"references-count":55,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["793"],"URL":"https:\/\/doi.org\/10.1007\/s41109-026-00793-4","relation":{"has-preprint":[{"id-type":"doi","id":"10.21203\/rs.3.rs-7973071\/v1","asserted-by":"object"}]},"ISSN":["2364-8228"],"issn-type":[{"value":"2364-8228","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,4,2]]},"assertion":[{"value":"28 October 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 March 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 April 2026","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 competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"37"}}