{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,15]],"date-time":"2026-04-15T04:55:02Z","timestamp":1776228902033,"version":"3.50.1"},"reference-count":78,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2026,2,18]],"date-time":"2026-02-18T00:00:00Z","timestamp":1771372800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2026,2,18]],"date-time":"2026-02-18T00:00:00Z","timestamp":1771372800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Netw Syst Manage"],"published-print":{"date-parts":[[2026,4]]},"DOI":"10.1007\/s10922-025-10026-1","type":"journal-article","created":{"date-parts":[[2026,2,18]],"date-time":"2026-02-18T10:03:29Z","timestamp":1771409009000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["SereWay: Toward SEcurity and REliability Benchmarking for the RailWAY IIoT"],"prefix":"10.1007","volume":"34","author":[{"given":"Alessandra","family":"Rizzardi","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Raffaele","family":"Della Corte","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jes\u00fas F.","family":"Cevallos M.","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Simona","family":"De Vivo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sabrina","family":"Sicari","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Domenico","family":"Cotroneo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Alberto","family":"Coen-Porisini","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,2,18]]},"reference":[{"key":"10026_CR1","doi-asserted-by":"crossref","unstructured":"M\u0142y\u0144czak, J., Toru\u0144, A., Bester, L.: European rail traffic management system (ertms). In: Intelligent Transportation Systems\u2013Problems and Perspectives, pp. 217\u2013242 (2015)","DOI":"10.1007\/978-3-319-19150-8_7"},{"key":"10026_CR2","doi-asserted-by":"crossref","unstructured":"Long Li, Li, L.: An introduction to the european rail traffic management system. In: International Conference of Logistics Engineering and Management, pp. 488\u2013492 (2012)","DOI":"10.1061\/9780784412602.0076"},{"key":"10026_CR3","unstructured":"Parlement Europ\u00e9en et Conseil de l\u2019Union Europ\u00e9enne: Directive (UE) 2016\/797 du Parlement Europ\u00e9en et du Conseil du 11 mai 2016 relative \u00e0 l\u2019interop\u00e9rabilit\u00e9 du syst\u00e8me ferroviaire au sein de l\u2019Union europ\u00e9enne (refonte). Texte pr\u00e9sentant de l\u2019int\u00e9r\u00eat pour l\u2019EEE (2016). https:\/\/eur-lex.europa.eu\/legal-content\/FR\/TXT\/PDF\/?uri=CELEX:32016L0797&rid=1"},{"key":"10026_CR4","doi-asserted-by":"crossref","unstructured":"Hu, J., Hu, J., Liu, G., Liu, G., Li, Y., Li, Y., Ma, Z., Ma, Z., Wang, W., Wang, W., Liang, C., Liang, C., Yu, F.R., Yu, F., Fan, P., Fan, P.: Off-network communications for future railway mobile communication systems: challenges and opportunities. IEEE Communications Magazine, pp. 1\u20137 (2022)","DOI":"10.1109\/MCOM.001.2101081"},{"issue":"18","key":"10026_CR5","doi-asserted-by":"publisher","first-page":"9003","DOI":"10.3390\/app12189003","volume":"12","author":"A Gonz\u00e1lez-Plaza","year":"2022","unstructured":"Gonz\u00e1lez-Plaza, A., Gonz\u00e1lez-Plaza, A., Cantarero, R.G., Cantarero, R.G., Banda, R.B.A., Banda, R.B.A., Briso-Rodriguez, C., Briso-Rodr\u00edguez, C.: 5G based on MNOs for critical railway signalling services: future railway mobile communication system. Appl. Sci. 12(18), 9003\u20139003 (2022)","journal-title":"Appl. Sci."},{"issue":"10","key":"10026_CR6","doi-asserted-by":"publisher","first-page":"10934","DOI":"10.1007\/s10489-021-03004-y","volume":"52","author":"O Serradilla","year":"2022","unstructured":"Serradilla, O., Zugasti, E., Rodriguez, J., Zurutuza, U.: Deep learning models for predictive maintenance: a survey, comparison, challenges and prospects. Appl. Intell. 52(10), 10934\u201310964 (2022)","journal-title":"Appl. Intell."},{"issue":"22","key":"10026_CR7","doi-asserted-by":"publisher","first-page":"7518","DOI":"10.3390\/s21227518","volume":"21","author":"S Latif","year":"2021","unstructured":"Latif, S.: Deep learning for the industrial internet of things (iiot): a comprehensive survey of techniques, implementation frameworks, potential applications, and future directions. Sensors 21(22), 7518 (2021)","journal-title":"Sensors"},{"issue":"1","key":"10026_CR8","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1007\/s00521-022-08017-3","volume":"35","author":"J Yu","year":"2023","unstructured":"Yu, J., Zhang, Y.: Challenges and opportunities of deep learning-based process fault detection and diagnosis: a review. Neural Comput. Appl. 35(1), 211\u2013252 (2023)","journal-title":"Neural Comput. Appl."},{"key":"10026_CR9","doi-asserted-by":"crossref","unstructured":"De\u00a0Vivo, S., Obaidat, I., Dai, D., Liguori, P.: Ddoshield-iot: A testbed for simulating and lightweight detection of iot botnet ddos attacks. In: 2024 54th Annual IEEE\/IFIP International Conference on Dependable Systems and Networks Workshops (DSN-W), pp. 1\u20138 (2024). IEEE","DOI":"10.1109\/DSN-W60302.2024.00014"},{"issue":"8","key":"10026_CR10","doi-asserted-by":"publisher","first-page":"378","DOI":"10.3390\/a16080378","volume":"16","author":"V Demertzi","year":"2023","unstructured":"Demertzi, V., Demertzis, S., Demertzis, K.: An overview of privacy dimensions on the industrial internet of things (iiot). Algorithms 16(8), 378 (2023)","journal-title":"Algorithms"},{"key":"10026_CR11","unstructured":"Lu, Y., et al.: Machine learning for synthetic data generation: a review. arXiv preprint arXiv:2302.04062 (2023)"},{"issue":"7","key":"10026_CR12","doi-asserted-by":"publisher","first-page":"388","DOI":"10.3390\/info14070388","volume":"14","author":"KT Chui","year":"2023","unstructured":"Chui, K.T.: A survey of internet of things and cyber-physical systems: standards, algorithms, applications, security, challenges, and future directions. Information 14(7), 388 (2023)","journal-title":"Information"},{"issue":"1","key":"10026_CR13","doi-asserted-by":"publisher","first-page":"551","DOI":"10.1109\/TII.2021.3133625","volume":"19","author":"Y-T Chen","year":"2021","unstructured":"Chen, Y.-T., Hsu, C.-Y., Yu, C.-M., Barhamgi, M., Perera, C.: On the private data synthesis through deep generative models for data scarcity of industrial internet of things. IEEE Trans. Ind. Inf. 19(1), 551\u2013560 (2021)","journal-title":"IEEE Trans. Ind. Inf."},{"key":"10026_CR14","doi-asserted-by":"crossref","unstructured":"Rizzardi, A., Della Corte, R., Cevallos\u00a0M., J.F., De\u00a0Vivo, S., Orbinato, V., Sicari, S., Cotroneo, D., Coen-Porisisni, A.: Open-FARI: An open-source testbed for federated anomaly detection in the railway IIoT. In: To Appear in Proc. of International Wireless Communications & Mobile Computing Conference (IWCMC 2025)","DOI":"10.1109\/IWCMC65282.2025.11059481"},{"issue":"2","key":"10026_CR15","doi-asserted-by":"publisher","first-page":"513","DOI":"10.1007\/s13042-022-01647-y","volume":"14","author":"J Wen","year":"2023","unstructured":"Wen, J., Zhang, Z., Lan, Y., Cui, Z., Cai, J., Zhang, W.: A survey on federated learning: challenges and applications. Int. J. Mach. Learn. Cybern. 14(2), 513\u2013535 (2023)","journal-title":"Int. J. Mach. Learn. Cybern."},{"key":"10026_CR16","doi-asserted-by":"publisher","first-page":"100078","DOI":"10.1016\/j.commtr.2022.100078","volume":"2","author":"Z Wang","year":"2022","unstructured":"Wang, Z., Wang, Z., Liu, X., Liu, X.: Cyber security of railway cyber-physical system (CPS) \u2013 a risk management methodology. Commun. Transp. Res. 2, 100078\u2013100078 (2022)","journal-title":"Commun. Transp. Res."},{"key":"10026_CR17","doi-asserted-by":"crossref","unstructured":"Kour, R., Patwardhan, A., Thaduri, A., Karim, R.: A review on cybersecurity in railways. In: Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit (2022)","DOI":"10.1177\/09544097221089389"},{"key":"10026_CR18","doi-asserted-by":"crossref","unstructured":"CBTC test simulation bench: J. M. Mera, Mera, J.M., I. G\u00f3mez-Rey, I. G\u00f3mez-Rey, I. G\u00f3mez-Rey, G\u00f3mez-Rey, I., G\u00f3mez-Rey, I., Everardo Rodrigo, Rodrigo, E., E. Rodrigo, E. Rodrigo, Rodrigo, E. WIT Transa. Built Environ. 114, 485\u2013495 (2010)","DOI":"10.2495\/CR100451"},{"issue":"4","key":"10026_CR19","doi-asserted-by":"publisher","first-page":"6353","DOI":"10.1109\/JIOT.2019.2919066","volume":"6","author":"S Kim","year":"2019","unstructured":"Kim, S., Won, Y., Park, I.-H., Eun, Y., Park, K.-J.: Cyber-physical vulnerability analysis of communication-based train control. IEEE Internet Things J. 6(4), 6353\u20136362 (2019)","journal-title":"IEEE Internet Things J."},{"issue":"9","key":"10026_CR20","first-page":"61","volume":"3","author":"J Xu","year":"2015","unstructured":"Xu, J., Chen, L., Gao, W., Zhao, M.: CBTC simulation platform design and study. J. Comput. Chem. 3(9), 61\u201367 (2015)","journal-title":"J. Comput. Chem."},{"key":"10026_CR21","doi-asserted-by":"crossref","unstructured":"Neema, H., Potteiger, B., Koutsoukos, X., Tang, C., Stouffer, K.: Metrics-driven evaluation of cybersecurity for critical railway infrastructure. In: 2018 Resilience week (RWS), pp. 155\u2013161 (2018)","DOI":"10.1109\/RWEEK.2018.8473542"},{"key":"10026_CR22","unstructured":"Caprino, G.: Train director version 3.9.10. https:\/\/www.backerstreet.com\/traindir\/en\/trdireng.php. Accessed on 2023-05-08 (2023)"},{"key":"10026_CR23","unstructured":"Neema, H., Nine, H., Hemingway, G., Sztipanovits, J., Karsai, G.: Rapid synthesis of multi-model simulations for computational experiments in c2 (2009). Technical report"},{"key":"10026_CR24","doi-asserted-by":"crossref","unstructured":"Neema, H., Koutsoukos, X., Potteiger, B., Tang, C., Stouffer, K.: Simulation testbed for railway infrastructure security and resilience evaluation. In: Proceedings of the 7th symposium on hot topics in the science of security, pp. 1\u20138. ACM, Lawrence Kansas (2020)","DOI":"10.1145\/3384217.3385623"},{"key":"10026_CR25","doi-asserted-by":"crossref","unstructured":"Teo, Z.-T., Tran, B. A. N., Lakshminarayana, S., Temple, W. G., Chen, B., Tan, R., Yau, D. K. Y.: SecureRails: Towards an open simulation platform for analyzing cyber-physical attacks in railways. In: 2016 IEEE Region 10 Conference (TENCON), pp. 95\u201398 (2016). ISSN: 2159-3450","DOI":"10.1109\/TENCON.2016.7847966"},{"key":"10026_CR26","unstructured":"Open Rails. https:\/\/www.openrails.org\/. Accessed on 17 March 2025 (2025)"},{"key":"10026_CR27","doi-asserted-by":"crossref","unstructured":"Bo\u017ei\u010d, J., Faustino, A.R., Radovi\u010d, B., Canini, M., Pejovi\u0107, V.: Where is the testbed for my federated learning research? arXiv preprint arXiv:2407.14154 (2024)","DOI":"10.1109\/SEC62691.2024.00027"},{"key":"10026_CR28","doi-asserted-by":"crossref","unstructured":"Symeonides, M., Nikolaidis, F., Trihinas, D., Pallis, G., Dikaiakos, M.D., Bilas, A.: Fedbed: Benchmarking federated learning over virtualized edge testbeds. In: Proceedings of the IEEE\/ACM 16th International Conference on Utility and Cloud Computing, pp. 1\u201310 (2023)","DOI":"10.1145\/3603166.3632138"},{"key":"10026_CR29","doi-asserted-by":"crossref","unstructured":"Popovic, M., Popovic, M., Kastelan, I., Djukic, M., Ghilezan, S.: A simple python testbed for federated learning algorithms. In: 2023 Zooming Innovation in Consumer Technologies Conference (ZINC), pp. 148\u2013153 (2023)","DOI":"10.1109\/ZINC58345.2023.10173859"},{"key":"10026_CR30","doi-asserted-by":"crossref","unstructured":"Popovic, M., Popovic, M., Kastelan, I., Djukic, M., Basicevic, I., Vasiljevic, P.: Micropython testbed for federated learning algorithms. In: 32nd Telecommunications Forum (TELFOR), pp. 1\u20134 (2024). IEEE","DOI":"10.1109\/TELFOR63250.2024.10819071"},{"key":"10026_CR31","doi-asserted-by":"crossref","unstructured":"Callebaut, G., Van\u00a0Mulders, J., Ottoy, G., Delabie, D., Cox, B., Stevens, N., Perre, L.: Techtile\u2013open 6g r &d testbed for communication, positioning, sensing, wpt and federated learning. In: 2022 Joint European Conference on Networks and Communications & 6G Summit (EuCNC\/6G Summit), pp. 417\u2013422 (2022). IEEE","DOI":"10.1109\/EuCNC\/6GSummit54941.2022.9815696"},{"key":"10026_CR32","doi-asserted-by":"crossref","unstructured":"Li, B., Su, N., Ying, C., Wang, F.: Plato: An open-source research framework for production federated learning. In: Proceedings of the ACM Turing Award Celebration Conference - China 2023. ACM TURC \u201923, pp. 1\u20132. Association for Computing Machinery, New York, NY, USA (2023)","DOI":"10.1145\/3603165.3607364"},{"issue":"3","key":"10026_CR33","doi-asserted-by":"publisher","first-page":"204","DOI":"10.1109\/TNSM.2023.3329442","volume":"21","author":"L Gioacchini","year":"2024","unstructured":"Gioacchini, L., Paoloni, A., Rinzivillo, S.: Cross-network embeddings transfer for traffic analysis. IEEE Trans. Netw. Serv. Manag. 21(3), 204\u2013215 (2024). https:\/\/doi.org\/10.1109\/TNSM.2023.3329442","journal-title":"IEEE Trans. Netw. Serv. Manag."},{"issue":"1","key":"10026_CR34","doi-asserted-by":"publisher","first-page":"82","DOI":"10.1109\/TNSM.2023.3287430","volume":"21","author":"G Bovenzi","year":"2024","unstructured":"Bovenzi, G., Palmieri, F., Ficco, M., Castiglione, A.: Benchmarking class incremental learning in deep learning traffic classification. IEEE Trans. Netw. Serv. Manag. 21(1), 82\u201395 (2024). https:\/\/doi.org\/10.1109\/TNSM.2023.3287430","journal-title":"IEEE Trans. Netw. Serv. Manag."},{"key":"10026_CR35","unstructured":"Fidelis, E.C., Reway, F., Ribeiro, H., Campos, P.L., Huber, W., Icking, C., Faria, L.A., Sch\u00f6n, T.: Generation of realistic synthetic raw radar data for automated driving applications using generative adversarial networks. arXiv preprint arXiv:2308.02632 (2023)"},{"key":"10026_CR36","doi-asserted-by":"crossref","unstructured":"Yang, Z., Chai, Y., Anguelov, D., Zhou, Y., Sun, P., Erhan, D., Rafferty, S., Kretzschmar, H.: Surfelgan: Synthesizing realistic sensor data for autonomous driving. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 11118\u201311127 (2020)","DOI":"10.1109\/CVPR42600.2020.01113"},{"key":"10026_CR37","doi-asserted-by":"crossref","unstructured":"Alzantot, M., Chakraborty, S., Srivastava, M.: Sensegen: A deep learning architecture for synthetic sensor data generation. In: 2017 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops), pp. 188\u2013193 (2017). IEEE","DOI":"10.1109\/PERCOMW.2017.7917555"},{"issue":"10","key":"10026_CR38","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3559540","volume":"55","author":"E Brophy","year":"2023","unstructured":"Brophy, E., Wang, Z., She, Q., Ward, T.: Generative adversarial networks in time series: A systematic literature review. ACM Comput. Surv. 55(10), 1\u201331 (2023)","journal-title":"ACM Comput. Surv."},{"key":"10026_CR39","unstructured":"Yang, Y., et al.: A survey on diffusion models for time series and spatio-temporal data. arXiv preprint arXiv:2404.18886 (2024)"},{"key":"10026_CR40","doi-asserted-by":"crossref","unstructured":"Wang, X., : An observed value consistent diffusion model for imputing missing values in multivariate time series. In: Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp. 2409\u20132418 (2023)","DOI":"10.1145\/3580305.3599257"},{"issue":"7","key":"10026_CR41","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/LSENS.2023.3290209","volume":"7","author":"F Romanelli","year":"2023","unstructured":"Romanelli, F., Martinelli, F.: Synthetic sensor data generation exploiting deep learning techniques and multimodal information. IEEE Sens. Lett. 7(7), 1\u20134 (2023)","journal-title":"IEEE Sens. Lett."},{"issue":"22","key":"10026_CR42","doi-asserted-by":"publisher","first-page":"7389","DOI":"10.3390\/s24227389","volume":"24","author":"S Alabdulwahab","year":"2024","unstructured":"Alabdulwahab, S., Kim, Y.-T., Son, Y.: Privacy-preserving synthetic data generation method for IoT-sensor network IDS using CTGAN. Sensors (Basel, Switzerland) 24(22), 7389 (2024)","journal-title":"Sensors (Basel, Switzerland)"},{"key":"10026_CR43","doi-asserted-by":"crossref","unstructured":"Savran, E., Karpat, F.: Synthetic data generation using copula model and driving behavior analysis. Ain Shams Eng. J., 103060 (2024)","DOI":"10.1016\/j.asej.2024.103060"},{"issue":"7","key":"10026_CR44","doi-asserted-by":"publisher","first-page":"2521","DOI":"10.1109\/JBHI.2020.3040551","volume":"25","author":"D Silva","year":"2020","unstructured":"Silva, D., Leonhardt, S., Antink, C.H.: Copula-based data augmentation on a deep learning architecture for cardiac sensor fusion. IEEE J. Biomed. Health Inform. 25(7), 2521\u20132532 (2020)","journal-title":"IEEE J. Biomed. Health Inform."},{"issue":"7","key":"10026_CR45","doi-asserted-by":"publisher","first-page":"1601","DOI":"10.3390\/electronics12071601","volume":"12","author":"JP Restrepo","year":"2023","unstructured":"Restrepo, J.P., Rivera, J.C., Laniado, H., Osorio, P., Becerra, O.A.: Nonparametric generation of synthetic data using copulas. Electronics 12(7), 1601 (2023)","journal-title":"Electronics"},{"key":"10026_CR46","doi-asserted-by":"publisher","first-page":"401","DOI":"10.1016\/j.ymssp.2016.10.034","volume":"87","author":"J Liu","year":"2017","unstructured":"Liu, J., Li, Y.-F., Zio, E.: A svm framework for fault detection of the braking system in a high speed train. Mech. Syst. Signal Process. 87, 401\u2013409 (2017)","journal-title":"Mech. Syst. Signal Process."},{"key":"10026_CR47","doi-asserted-by":"publisher","DOI":"10.1016\/j.measurement.2020.108779","volume":"172","author":"J Liu","year":"2021","unstructured":"Liu, J., Hu, Y., Yang, S.: A SVM-based framework for fault detection in high-speed trains. Measurement 172, 108779 (2021)","journal-title":"Measurement"},{"key":"10026_CR48","doi-asserted-by":"crossref","unstructured":"Liu, J., Liu, J., Zio, E., Zio, E.: KNN-FSVM for fault detection in high-speed trains. In: International Conference on Prognostics and Health Management, pp. 1\u20137 (2018)","DOI":"10.1109\/ICPHM.2018.8448688"},{"issue":"24","key":"10026_CR49","doi-asserted-by":"publisher","first-page":"1330","DOI":"10.1016\/j.ifacol.2018.09.562","volume":"51","author":"S Zoljic-Beglerovic","year":"2018","unstructured":"Zoljic-Beglerovic, S., Stettinger, G., Luber, B., Horn, M.: Railway suspension system fault diagnosis using cubature Kalman filter techniques. IFAC-PapersOnLine 51(24), 1330\u20131335 (2018)","journal-title":"IFAC-PapersOnLine"},{"key":"10026_CR50","doi-asserted-by":"crossref","unstructured":"Qin, L., Yang, G., He, W.: Generalized Shannon entropy sparse wavelet packet transform for fault detection of traction motor bearings in high-speed trains. Structural Health Monitoring (2024)","DOI":"10.1177\/14759217241245320"},{"issue":"4","key":"10026_CR51","doi-asserted-by":"publisher","first-page":"679","DOI":"10.1109\/TAI.2022.3172896","volume":"4","author":"L Guo","year":"2023","unstructured":"Guo, L., Li, R., Jiang, B.: Fault detection and diagnosis using statistic feature and improved broad learning for traction systems in high-speed trains. IEEE Trans. Artif. Intell. 4(4), 679\u2013688 (2023)","journal-title":"IEEE Trans. Artif. Intell."},{"issue":"9","key":"10026_CR52","doi-asserted-by":"publisher","first-page":"2797","DOI":"10.1007\/s12555-022-0241-2","volume":"21","author":"C Cheng","year":"2023","unstructured":"Cheng, C., Sun, X., Shao, J., Chen, H., Chen, S.: Just-in-time learning-aided nonlinear fault detection for traction systems of high-speed trains. Int. J. Control Autom. Syst. 21(9), 2797\u20132809 (2023)","journal-title":"Int. J. Control Autom. Syst."},{"issue":"6","key":"10026_CR53","doi-asserted-by":"publisher","first-page":"4819","DOI":"10.1109\/TVT.2018.2818538","volume":"67","author":"H Chen","year":"2018","unstructured":"Chen, H., Chen, H., Jiang, B., Jiang, B., Jiang, B., Jiang, B., Jiang, B., Jiang, B., Jiang, B., Lu, N., Lu, N., Mao, Z., Mao, Z.: Deep PCA based real-time incipient fault detection and diagnosis methodology for electrical drive in high-speed trains. IEEE Trans. Veh. Technol. 67(6), 4819\u20134830 (2018)","journal-title":"IEEE Trans. Veh. Technol."},{"issue":"14","key":"10026_CR54","doi-asserted-by":"publisher","first-page":"6392","DOI":"10.3390\/s23146392","volume":"23","author":"S Sun","year":"2023","unstructured":"Sun, S., Zhang, S., Wang, W.: A new monitoring technology for bearing fault detection in high-speed trains. Sensors (Basel, Switzerland) 23(14), 6392 (2023)","journal-title":"Sensors (Basel, Switzerland)"},{"key":"10026_CR55","doi-asserted-by":"crossref","unstructured":"Wang, S., Ju, Y., Xie, P., Cheng, C.: Fault detection using generalized autoencoder with neighborhood restriction for electrical drive systems of high-speed trains. Control Eng. Pract. (2024)","DOI":"10.1016\/j.conengprac.2023.105804"},{"issue":"4","key":"10026_CR56","doi-asserted-by":"publisher","first-page":"2106","DOI":"10.1109\/TII.2017.2683528","volume":"13","author":"H Hu","year":"2017","unstructured":"Hu, H., Tang, B., Gong, X., Wei, W., Wang, H.: Intelligent fault diagnosis of the high-speed train with big data based on deep neural networks. IEEE Trans. Ind. Inf. 13(4), 2106\u20132116 (2017)","journal-title":"IEEE Trans. Ind. Inf."},{"issue":"4","key":"10026_CR57","doi-asserted-by":"publisher","first-page":"689","DOI":"10.1109\/TAI.2022.3177387","volume":"4","author":"C Cheng","year":"2023","unstructured":"Cheng, C., Xuedong Li, P., Xie, X.Y.: Transfer-learning-aided fault detection for traction drive systems of high-speed trains. IEEE Trans. Artif. Intell. 4(4), 689\u2013697 (2023)","journal-title":"IEEE Trans. Artif. Intell."},{"issue":"3","key":"10026_CR58","doi-asserted-by":"publisher","first-page":"1251","DOI":"10.3390\/app11031251","volume":"11","author":"K Zhang","year":"2021","unstructured":"Zhang, K., Huang, W., Hou, X., Xu, J., Su, R., Xu, H.: A fault diagnosis and visualization method for high-speed train based on edge and cloud collaboration. Appl. Sci. 11(3), 1251 (2021)","journal-title":"Appl. Sci."},{"issue":"8","key":"10026_CR59","doi-asserted-by":"publisher","first-page":"4790","DOI":"10.3390\/app13084790","volume":"13","author":"S Xie","year":"2023","unstructured":"Xie, S., Tan, H., Yang, C., Yan, H.: A review of fault diagnosis methods for key systems of the high-speed train. Appl. Sci. 13(8), 4790 (2023)","journal-title":"Appl. Sci."},{"issue":"2","key":"10026_CR60","doi-asserted-by":"publisher","first-page":"450","DOI":"10.1109\/TITS.2019.2897583","volume":"21","author":"H Chen","year":"2020","unstructured":"Chen, H., Chen, H., Jiang, B., Jiang, B., Jiang, B., Jiang, B.: A review of fault detection and diagnosis for the traction system in high-speed trains. IEEE Trans. Intell. Transp. Syst. 21(2), 450\u2013465 (2020)","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"10026_CR61","doi-asserted-by":"crossref","unstructured":"Kour, R., Patwardhan, A., Thaduri, A., Karim, R.: A review on cybersecurity in railways. Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit 237(1), 3\u201320 (2023)","DOI":"10.1177\/09544097221089389"},{"issue":"3","key":"10026_CR62","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10922-023-09722-7","volume":"31","author":"O Jullian","year":"2023","unstructured":"Jullian, O., Otero, B., Rodriguez, E.: Deep-learning based detection for cyber-attacks in iot networks: a distributed attack detection framework. J. Netw. Syst. Manage. 31(3), 1\u201322 (2023). https:\/\/doi.org\/10.1007\/s10922-023-09722-7","journal-title":"J. Netw. Syst. Manage."},{"key":"10026_CR63","doi-asserted-by":"publisher","unstructured":"Fr\"ohlich, A.A., Bogo, F., Oliveira, R.: A secure iiot gateway architecture based on trusted execution environments. J. Netw. Syst. Manag. 31(4), 1\u201317 (2023). https:\/\/doi.org\/10.1007\/s10922-023-09723-6","DOI":"10.1007\/s10922-023-09723-6"},{"issue":"7","key":"10026_CR64","doi-asserted-by":"publisher","first-page":"5558","DOI":"10.1109\/JIOT.2020.3032093","volume":"8","author":"M Al-Hawawreh","year":"2020","unstructured":"Al-Hawawreh, M., Sitnikova, E.: Developing a security testbed for industrial internet of things. IEEE Internet Things J. 8(7), 5558\u20135573 (2020)","journal-title":"IEEE Internet Things J."},{"key":"10026_CR65","doi-asserted-by":"publisher","first-page":"165130","DOI":"10.1109\/ACCESS.2020.3022862","volume":"8","author":"A Alsaedi","year":"2020","unstructured":"Alsaedi, A., Moustafa, N., Tari, Z., Mahmood, A., Anwar, A.: Ton_iot telemetry dataset: a new generation dataset of iot and iiot for data-driven intrusion detection systems. IEEE Access 8, 165130\u2013165150 (2020)","journal-title":"IEEE Access"},{"key":"10026_CR66","doi-asserted-by":"publisher","first-page":"40281","DOI":"10.1109\/ACCESS.2022.3165809","volume":"10","author":"MA Ferrag","year":"2022","unstructured":"Ferrag, M.A., Friha, O., Hamouda, D., Maglaras, L., Janicke, H.: Edge-iiotset: A new comprehensive realistic cyber security dataset of iot and iiot applications for centralized and federated learning. IEEE Access 10, 40281\u201340306 (2022)","journal-title":"IEEE Access"},{"issue":"4","key":"10026_CR67","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3503920","volume":"3","author":"L Axon","year":"2022","unstructured":"Axon, L., Fletcher, K., Scott, A.S., Stolz, M., Hannigan, R., Kaafarani, A.E., Goldsmith, M., Creese, S.: Emerging cybersecurity capability gaps in the industrial internet of things: overview and research agenda. Digital Threats: Res. Pract. 3(4), 1\u201327 (2022)","journal-title":"Digital Threats: Res. Pract."},{"key":"10026_CR68","doi-asserted-by":"publisher","DOI":"10.1016\/j.ssci.2023.106188","volume":"164","author":"M Blumenfeld","year":"2023","unstructured":"Blumenfeld, M., Lin, C.-Y., Jack, A., Abdurrahman, U.T., Gerstein, T., Barkan, C.P.: Towards measuring national railways\u2019 safety through a benchmarking framework of transparency and published data. Saf. Sci. 164, 106188 (2023)","journal-title":"Saf. Sci."},{"key":"10026_CR69","unstructured":"Simoni, B.: ERTMS: the European rail traffic management system. Accessed on 2026\/01\/02 16:16:45. https:\/\/voie-libre.com\/en\/ertms-european-rail-traffic-management-system\/"},{"key":"10026_CR70","unstructured":"European Rail Traffic Management System (ERTMS). ERTMS Organization. Accessed on 2026\/01\/02 16:16:45. https:\/\/www.era.europa.eu\/domains\/infrastructure\/european-rail-traffic-management-system-ertms_en"},{"key":"10026_CR71","doi-asserted-by":"crossref","unstructured":"Rizzardi, A., Della\u00a0Corte, R., Orbinato, V., Cevallos\u00a0M., J.F., De\u00a0Vivo, S., Sicari, S., Cotroneo, D., Coen-Porisini, A., : Railred: a node-red-based framework for modeling train control management systems. In: 2024 20th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob), pp. 671\u2013674 (2024). IEEE","DOI":"10.1109\/WiMob61911.2024.10770345"},{"key":"10026_CR72","unstructured":"McInnes, L., Healy, J., Melville, J.: UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction. arXiv. arXiv:1802.03426 (2020)"},{"key":"10026_CR73","doi-asserted-by":"crossref","unstructured":"Shahapure, K.R., Nicholas, C.: Cluster quality analysis using silhouette score. 2020 IEEE 7th international conference on data science and advanced analytics (DSAA), 747\u2013748 (2020)","DOI":"10.1109\/DSAA49011.2020.00096"},{"key":"10026_CR74","doi-asserted-by":"crossref","unstructured":"Jaworski, P., Durante, F., H\u00e4rdle, W.K., Rychlik, T. (eds.): Copula Theory and Its Applications: Proceedings of the Workshop Held in Warsaw, 25-26 September 2009. Lecture Notes in Statistics, vol. 198. Springer, Berlin, Heidelberg (2010)","DOI":"10.1007\/978-3-642-12465-5"},{"key":"10026_CR75","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Jiang, H., Zhao, H., Li, Y.: Federated learning-based edge computing for automatic train operation in communication-based train control systems. J. Supercomput., 1\u201319 (2024)","DOI":"10.1007\/s11227-024-06075-z"},{"key":"10026_CR76","unstructured":"McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273\u20131282 (2017). PMLR"},{"key":"10026_CR77","doi-asserted-by":"crossref","unstructured":"Wohlin, C., Runeson, P., H\u00f6st, M., Ohlsson, M.C., Regnell, B., Wessl\u00e9n, A., et al.: Experimentation in software engineering 236 (2012)","DOI":"10.1007\/978-3-642-29044-2"},{"key":"10026_CR78","unstructured":"Srock, A., Arhelger, T.: ERTMS Conference,: | Valenciennes W. Malfait & J, Hernandez Fernandez (2024)"}],"container-title":["Journal of Network and Systems Management"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10922-025-10026-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10922-025-10026-1","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10922-025-10026-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,15]],"date-time":"2026-04-15T04:03:29Z","timestamp":1776225809000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10922-025-10026-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,2,18]]},"references-count":78,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2026,4]]}},"alternative-id":["10026"],"URL":"https:\/\/doi.org\/10.1007\/s10922-025-10026-1","relation":{},"ISSN":["1064-7570","1573-7705"],"issn-type":[{"value":"1064-7570","type":"print"},{"value":"1573-7705","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,2,18]]},"assertion":[{"value":"10 April 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 November 2025","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 December 2025","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 February 2026","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 March 2026","order":6,"name":"change_date","label":"Change Date","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"Update","order":7,"name":"change_type","label":"Change Type","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The affiliations has been corrected","order":8,"name":"change_details","label":"Change Details","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors have no relevant financial or non-financial interests to disclose.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"52"}}