{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,16]],"date-time":"2025-02-16T05:04:09Z","timestamp":1739682249536,"version":"3.37.1"},"reference-count":34,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2025,2,15]],"date-time":"2025-02-15T00:00:00Z","timestamp":1739577600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,2,15]],"date-time":"2025-02-15T00:00:00Z","timestamp":1739577600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"DOI":"10.13039\/100009532","name":"Ministerstvo Vnitra \u010cesk\u00e9 Republiky","doi-asserted-by":"publisher","award":["J02010016"],"award-info":[{"award-number":["J02010016"]}],"id":[{"id":"10.13039\/100009532","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["SN COMPUT. SCI."],"DOI":"10.1007\/s42979-025-03673-3","type":"journal-article","created":{"date-parts":[[2025,2,15]],"date-time":"2025-02-15T08:47:36Z","timestamp":1739609256000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["The Effect of Generating Synthetic Data in Smart City Network Systems"],"prefix":"10.1007","volume":"6","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2547-0629","authenticated-orcid":false,"given":"Pavel","family":"\u010cech","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Daniela","family":"Ponce","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Peter","family":"Mikuleck\u00fd","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Andrea","family":"\u017dv\u00e1\u010dkov\u00e1","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Karel","family":"Mls","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tereza","family":"Ot\u010den\u00e1\u0161kov\u00e1","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Petr","family":"Tu\u010dn\u00edk","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,2,15]]},"reference":[{"issue":"3","key":"3673_CR1","doi-asserted-by":"publisher","first-page":"676","DOI":"10.1016\/j.dcan.2022.09.009","volume":"10","author":"A Azab","year":"2024","unstructured":"Azab A, Khasawneh M, Alrabaee S, Choo K-KR, Sarsour M. Network traffic classification: techniques, datasets, and challenges. Digit Commun Netw. 2024;10(3):676\u201392. https:\/\/doi.org\/10.1016\/j.dcan.2022.09.009.","journal-title":"Digit Commun Netw"},{"key":"3673_CR2","doi-asserted-by":"publisher","DOI":"10.3390\/electronics13061108","author":"RH Serag","year":"2024","unstructured":"Serag RH, Abdalzaher MS, Elsayed HAEA, Sobh M, Krichen M, Salim MM. Machine-learning-based traffic classification in software-defined networks. Electronics. 2024. https:\/\/doi.org\/10.3390\/electronics13061108.","journal-title":"Electronics"},{"key":"3673_CR3","doi-asserted-by":"publisher","DOI":"10.3390\/s24092746","author":"C Strickland","year":"2024","unstructured":"Strickland C, Zakar M, Saha C, Nejad SS, Tasnim N, Lizotte DJ, Haque A. DRL-GAN: a hybrid approach for binary and multiclass network intrusion detection. Sensors. 2024. https:\/\/doi.org\/10.3390\/s24092746.","journal-title":"Sensors"},{"key":"3673_CR4","doi-asserted-by":"publisher","first-page":"17945","DOI":"10.1109\/access.2024.3360879","volume":"12","author":"FS Melicias","year":"2024","unstructured":"Melicias FS, Ribeiro TFR, Rabadao C, Santos L, Costa RLDC. GPT and interpolation-based data augmentation for multiclass intrusion detection in IIoT. IEEE Access. 2024;12:17945\u201365. https:\/\/doi.org\/10.1109\/access.2024.3360879.","journal-title":"IEEE Access"},{"issue":"1","key":"3673_CR5","doi-asserted-by":"publisher","first-page":"217","DOI":"10.1007\/s00500-023-09331-1","volume":"28","author":"M Zheng","year":"2024","unstructured":"Zheng M, Ma K, Wang F, Hu X, Yu Q, Guo L, Chen F. Which standard classification algorithm has more stable performance for imbalanced network traffic data? Soft Comput. 2024;28(1):217\u201334. https:\/\/doi.org\/10.1007\/s00500-023-09331-1.","journal-title":"Soft Comput"},{"key":"3673_CR6","doi-asserted-by":"publisher","DOI":"10.1016\/j.cose.2023.103432","author":"A Srivastava","year":"2023","unstructured":"Srivastava A, Sinha D, Kumar V. WCGAN-GP based synthetic attack data generation with GA based feature selection for IDS. Comput Secur. 2023. https:\/\/doi.org\/10.1016\/j.cose.2023.103432.","journal-title":"Comput Secur"},{"key":"3673_CR7","doi-asserted-by":"publisher","DOI":"10.1016\/j.csi.2021.103545","author":"AA Afuwape","year":"2021","unstructured":"Afuwape AA, Xu Y, Anajemba JH, Srivastava G. Performance evaluation of secured network traffic classification using a machine learning approach. Comput Stand Interfaces. 2021. https:\/\/doi.org\/10.1016\/j.csi.2021.103545.","journal-title":"Comput Stand Interfaces"},{"key":"3673_CR8","doi-asserted-by":"publisher","first-page":"1","DOI":"10.7717\/peerj-cs.820","volume":"8","author":"HA Ahmed","year":"2022","unstructured":"Ahmed HA, Hameed A, Bawany NZ. Network intrusion detection using oversampling technique and machine learning algorithms. PeerJ Comput Sci. 2022;8:1\u201319. https:\/\/doi.org\/10.7717\/peerj-cs.820.","journal-title":"PeerJ Comput Sci"},{"key":"3673_CR9","doi-asserted-by":"publisher","DOI":"10.1016\/j.iot.2022.100615","author":"H Ahmetoglu","year":"2022","unstructured":"Ahmetoglu H, Das R. A comprehensive review on detection of cyber-attacks: data sets, methods, challenges, and future research directions. Internet Things. 2022. https:\/\/doi.org\/10.1016\/j.iot.2022.100615.","journal-title":"Internet Things"},{"key":"3673_CR10","doi-asserted-by":"publisher","first-page":"129612","DOI":"10.1109\/access.2022.3228507","volume":"10","author":"D Cullen","year":"2022","unstructured":"Cullen D, Halladay J, Briner N, Basnet R, Bergen J, Doleck T. Evaluation of synthetic data generation techniques in the domain of anonymous traffic classification. IEEE Access. 2022;10:129612\u201325. https:\/\/doi.org\/10.1109\/access.2022.3228507.","journal-title":"IEEE Access"},{"key":"3673_CR11","doi-asserted-by":"publisher","DOI":"10.1016\/j.jii.2023.100466","author":"I Fosic","year":"2023","unstructured":"Fosic I, Zagar D, Grgic K, Krizanovic V. Anomaly detection in netflow network traffic using supervised machine learning algorithms. J Ind Inf Integr. 2023. https:\/\/doi.org\/10.1016\/j.jii.2023.100466.","journal-title":"J Ind Inf Integr"},{"key":"3673_CR12","doi-asserted-by":"publisher","DOI":"10.3390\/s22239326","author":"M Rodriguez","year":"2022","unstructured":"Rodriguez M, Alesanco A, Mehavilla L, Garcia J. Evaluation of machine learning techniques for traffic flow-based intrusion detection. Sensors. 2022. https:\/\/doi.org\/10.3390\/s22239326.","journal-title":"Sensors"},{"key":"3673_CR13","doi-asserted-by":"publisher","unstructured":"Xu S, Marwah M, Arlitt M, Ramakrishnan N. STAN: synthetic network traffic generation with generative neural models. In: Wang G, Ciptadi A, Ahmadzadeh A, editors. Deployable machine learning for security defense, MLHAT 2021. Communications in computer and information science, vol. 1482; 2021. p. 3\u201329. https:\/\/doi.org\/10.1007\/978-3-030-87839-9_1.","DOI":"10.1007\/978-3-030-87839-9_1"},{"key":"3673_CR14","doi-asserted-by":"publisher","DOI":"10.1016\/j.jisa.2024.103827","author":"S Wu","year":"2024","unstructured":"Wu S, Wang W, Ding Z. Detecting malicious DoH traffic: leveraging small sample analysis and adversarial networks for detection. J Inf Secur Appl. 2024. https:\/\/doi.org\/10.1016\/j.jisa.2024.103827.","journal-title":"J Inf Secur Appl"},{"issue":"16","key":"3673_CR15","doi-asserted-by":"publisher","first-page":"27715","DOI":"10.1109\/jiot.2024.3404808","volume":"11","author":"AGM Mengara","year":"2024","unstructured":"Mengara AGM, Yoo Y, Leung VCM. IoTSecUT: uncertainty-based hybrid deep learning approach for superior IoT security amidst evolving cyber threats. IEEE Internet Things J. 2024;11(16):27715\u201331. https:\/\/doi.org\/10.1109\/jiot.2024.3404808.","journal-title":"IEEE Internet Things J"},{"key":"3673_CR16","doi-asserted-by":"publisher","DOI":"10.1016\/j.cose.2024.103993","author":"M Wolf","year":"2024","unstructured":"Wolf M, Tritscher J, Landes D, Hotho A, Schloer D. Benchmarking of synthetic network data: reviewing challenges and approaches. Comput Secur. 2024. https:\/\/doi.org\/10.1016\/j.cose.2024.103993.","journal-title":"Comput Secur"},{"key":"3673_CR17","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijmedinf.2024.105413","author":"VB Vallevik","year":"2024","unstructured":"Vallevik VB, Babic A, Marshall SE, Elvatun S, Brogger HMB, Alagaratnam S, Edwin B, Veeraragavan NR, Befring AK, Nygard JF. Can I trust my fake data\u2014a comprehensive quality assessment framework for synthetic tabular data in healthcare. Int J Med Inform. 2024. https:\/\/doi.org\/10.1016\/j.ijmedinf.2024.105413.","journal-title":"Int J Med Inform"},{"key":"3673_CR18","doi-asserted-by":"publisher","DOI":"10.3390\/electronics13101965","author":"E Papadaki","year":"2024","unstructured":"Papadaki E, Vrahatis AG, Kotsiantis S. Exploring innovative approaches to synthetic tabular data generation. Electronics. 2024. https:\/\/doi.org\/10.3390\/electronics13101965.","journal-title":"Electronics"},{"key":"3673_CR19","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2024.128253","author":"E Budu","year":"2024","unstructured":"Budu E, Etminani K, Soliman A, Rognvaldsson T. Evaluation of synthetic electronic health records: a systematic review and experimental assessment. Neurocomputing. 2024. https:\/\/doi.org\/10.1016\/j.neucom.2024.128253.","journal-title":"Neurocomputing"},{"issue":"14, SI","key":"3673_CR20","doi-asserted-by":"publisher","first-page":"10123","DOI":"10.1007\/s00521-023-08459-3","volume":"35","author":"G Iglesias","year":"2023","unstructured":"Iglesias G, Talavera E, Gonzalez-Prieto A, Mozo A, Gomez-Canaval S. Data augmentation techniques in time series domain: a survey and taxonomy. Neural Comput Appl. 2023;35(14, SI):10123\u201345. https:\/\/doi.org\/10.1007\/s00521-023-08459-3.","journal-title":"Neural Comput Appl"},{"key":"3673_CR21","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2024.107881","author":"Y Akkem","year":"2024","unstructured":"Akkem Y, Biswas SK, Varanasi A. A comprehensive review of synthetic data generation in smart farming by using variational autoencoder and generative adversarial network. Eng Appl Artif Intell. 2024. https:\/\/doi.org\/10.1016\/j.engappai.2024.107881.","journal-title":"Eng Appl Artif Intell"},{"key":"3673_CR22","doi-asserted-by":"publisher","first-page":"64601","DOI":"10.1109\/access.2022.3177906","volume":"10","author":"MH Naveed","year":"2022","unstructured":"Naveed MH, Hashmi US, Tajved N, Sultan N, Imran A. Assessing deep generative models on time series network data. IEEE Access. 2022;10:64601\u201317. https:\/\/doi.org\/10.1109\/access.2022.3177906.","journal-title":"IEEE Access"},{"key":"3673_CR23","doi-asserted-by":"publisher","unstructured":"Nukavarapu SK, Ayyat M, Nadeem T. MirageNet\u2014towards a GAN-based framework for synthetic network traffic generation. In: 2022 IEEE global communications conference (GLOBECOM 2022). IEEE global communications conference. IEEE; 2022. p. 3089\u201395. https:\/\/doi.org\/10.1109\/globecom48099.2022.10001494.","DOI":"10.1109\/globecom48099.2022.10001494"},{"key":"3673_CR24","doi-asserted-by":"publisher","first-page":"28","DOI":"10.1016\/j.neucom.2022.04.053","volume":"493","author":"M Hernandez","year":"2022","unstructured":"Hernandez M, Epelde G, Alberdi A, Cilla R, Rankin D. Synthetic data generation for tabular health records: a review. Neurocomputing. 2022;493:28\u201345. https:\/\/doi.org\/10.1016\/j.neucom.2022.04.053.","journal-title":"Neurocomputing"},{"key":"3673_CR25","doi-asserted-by":"publisher","unstructured":"Xu K, Singh R, Fiore M, Marina MK, Bilen H, Usama M, Benn H, Ziemlicki C. SpectraGAN: spectrum based generation of city scale spatiotemporal mobile network traffic data. In: Proceedings of the 17th international conference on emerging networking experiments and technologies, CONEXT 2021. Assoc Comp Machinery; ACM SIGCOMM; 2021. p. 243\u201358. https:\/\/doi.org\/10.1145\/3485983.3494844.","DOI":"10.1145\/3485983.3494844"},{"key":"3673_CR26","doi-asserted-by":"publisher","unstructured":"Nigam A, Srivastava S. Generating realistic synthetic traffic data using conditional tabular generative adversarial networks for intelligent transportation systems. In: 2023 IEEE 26th international conference on intelligent transportation systems, ITSC. IEEE international conference on intelligent transportation systems-ITSC. IEEE; 2023. p. 2881\u20136. https:\/\/doi.org\/10.1109\/itsc57777.2023.10422234.","DOI":"10.1109\/itsc57777.2023.10422234"},{"key":"3673_CR27","doi-asserted-by":"publisher","DOI":"10.3390\/app131910951","author":"S Alabdulwahab","year":"2023","unstructured":"Alabdulwahab S, Kim Y-T, Seo A, Son Y. Generating synthetic dataset for ML-based IDS using CTGAN and feature selection to protect smart IoT environments. Appl Sci Basel. 2023. https:\/\/doi.org\/10.3390\/app131910951.","journal-title":"Appl Sci Basel"},{"key":"3673_CR28","doi-asserted-by":"publisher","DOI":"10.3390\/info12090375","author":"S Bourou","year":"2021","unstructured":"Bourou S, El Saer A, Velivassaki T-H, Voulkidis A, Zahariadis T. A review of tabular data synthesis using GANs on an IDS dataset. Information. 2021. https:\/\/doi.org\/10.3390\/info12090375.","journal-title":"Information"},{"key":"3673_CR29","doi-asserted-by":"publisher","first-page":"1539","DOI":"10.1016\/j.ins.2022.07.066","volume":"609","author":"A Gonzalez-Prieto","year":"2022","unstructured":"Gonzalez-Prieto A, Mozo A, Gomez-Canaval S, Talavera E. Improving the quality of generative models through Smirnov transformation. Inf Sci. 2022;609:1539\u201366. https:\/\/doi.org\/10.1016\/j.ins.2022.07.066.","journal-title":"Inf Sci"},{"key":"3673_CR30","doi-asserted-by":"publisher","first-page":"114936","DOI":"10.1109\/access.2023.3325727","volume":"11","author":"DK Kholgh","year":"2023","unstructured":"Kholgh DK, Kostakos P. PAC-GPT: a novel approach to generating synthetic network traffic with GPT-3. IEEE Access. 2023;11:114936\u201351. https:\/\/doi.org\/10.1109\/access.2023.3325727.","journal-title":"IEEE Access"},{"key":"3673_CR31","doi-asserted-by":"crossref","unstructured":"Moustafa N, Slay J. 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); Canberra, ACT, Australia, 2015. p. 1\u20136.","DOI":"10.1109\/MilCIS.2015.7348942"},{"key":"3673_CR32","doi-asserted-by":"publisher","unstructured":"Sharafaldin I, Habibi\u00a0Lashkari A, Ghorbani A. Toward generating a new intrusion detection dataset and intrusion traffic characterization. In: 4th International conference on information systems security and privacy (ICISSP); 2018. p. 108\u201316. https:\/\/doi.org\/10.5220\/0006639801080116.","DOI":"10.5220\/0006639801080116"},{"key":"3673_CR33","doi-asserted-by":"publisher","DOI":"10.3390\/s23135941","author":"ECP Neto","year":"2023","unstructured":"Neto ECP, Dadkhah S, Ferreira R, Zohourian A, Lu R, Ghorbani AA. Ciciot 2023: a real-time dataset and benchmark for large-scale attacks in IoT environment. Sensors. 2023. https:\/\/doi.org\/10.3390\/s23135941.","journal-title":"Sensors"},{"key":"3673_CR34","doi-asserted-by":"publisher","first-page":"40","DOI":"10.1007\/978-3-031-52426-4_3","volume-title":"Mobile, secure, and programmable networking","author":"P \u010cech","year":"2024","unstructured":"\u010cech P, Ponce D, Mikuleck\u00fd P, Mls K, \u017dv\u00e1\u010dkov\u00e1 A, Tu\u010dn\u00edk P, Ot\u010den\u00e1\u0161kov\u00e1 T. Generating synthetic data to improve intrusion detection in smart city network systems. In: Bouzefrane S, Banerjee S, Mourlin F, Boumerdassi S, Renault \u00c9, editors. Mobile, secure, and programmable networking. Cham: Springer; 2024. p. 40\u201351."}],"container-title":["SN Computer Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s42979-025-03673-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s42979-025-03673-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s42979-025-03673-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,2,15]],"date-time":"2025-02-15T08:47:42Z","timestamp":1739609262000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s42979-025-03673-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,2,15]]},"references-count":34,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2025,2]]}},"alternative-id":["3673"],"URL":"https:\/\/doi.org\/10.1007\/s42979-025-03673-3","relation":{},"ISSN":["2661-8907"],"issn-type":[{"value":"2661-8907","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,2,15]]},"assertion":[{"value":"12 November 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 January 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 February 2025","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 of the study are financially supported by the project \u201cApplication of Artificial Intelligence for Ensuring Cyber Security in Smart City\u201d, n. VJ02010016, granted by the Ministry of the Interior of the Czech Republic.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of Interest"}},{"value":"This is an observational study. The University of Hradec Kr\u00e1lov\u00e9 Research Ethics Committee has confirmed that no ethical approval is required.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Compliance with Ethical Standards"}},{"value":"Not applicable.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Research Involving Human and\/or Animals"}},{"value":"Not applicable.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed Consent"}}],"article-number":"174"}}