{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,28]],"date-time":"2025-03-28T07:35:31Z","timestamp":1743147331766,"version":"3.40.3"},"publisher-location":"Cham","reference-count":18,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031524257"},{"type":"electronic","value":"9783031524264"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"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":[],"published-print":{"date-parts":[[2024]]},"DOI":"10.1007\/978-3-031-52426-4_3","type":"book-chapter","created":{"date-parts":[[2024,1,24]],"date-time":"2024-01-24T06:02:28Z","timestamp":1706076148000},"page":"40-51","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Generating Synthetic Data to\u00a0Improve Intrusion Detection in\u00a0Smart City Network Systems"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2547-0629","authenticated-orcid":false,"given":"Pavel","family":"\u010cech","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5190-0453","authenticated-orcid":false,"given":"Daniela","family":"Ponce","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2595-3989","authenticated-orcid":false,"given":"Peter","family":"Mikuleck\u00fd","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7681-8277","authenticated-orcid":false,"given":"Karel","family":"Mls","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4092-9522","authenticated-orcid":false,"given":"Andrea","family":"\u017dv\u00e1\u010dkov\u00e1","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6705-8974","authenticated-orcid":false,"given":"Petr","family":"Tu\u010dn\u00edk","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5725-9302","authenticated-orcid":false,"given":"Tereza","family":"Ot\u010den\u00e1\u0161kov\u00e1","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,1,25]]},"reference":[{"key":"3_CR1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s40537-021-00496-w","volume":"8","author":"N AlDahoul","year":"2021","unstructured":"AlDahoul, N., Abdul Karim, H., Ba Wazir, A.S.: Model fusion of deep neural networks for anomaly detection. J. Big Data 8, 1\u201318 (2021)","journal-title":"J. Big Data"},{"key":"3_CR2","doi-asserted-by":"publisher","first-page":"249","DOI":"10.1016\/j.neunet.2018.07.011","volume":"106","author":"M Buda","year":"2018","unstructured":"Buda, M., Maki, A., Mazurowski, M.A.: A systematic study of the class imbalance problem in convolutional neural networks. Neural Netw. 106, 249\u2013259 (2018)","journal-title":"Neural Netw."},{"issue":"19","key":"3_CR3","doi-asserted-by":"publisher","first-page":"3482","DOI":"10.3390\/math10193482","volume":"10","author":"X Cao","year":"2022","unstructured":"Cao, X., Luo, Q., Wu, P.: Filter-GAN: imbalanced malicious traffic classification based on generative adversarial networks with filter. Mathematics 10(19), 3482 (2022)","journal-title":"Mathematics"},{"key":"3_CR4","doi-asserted-by":"publisher","DOI":"10.1016\/j.jnca.2022.103368","volume":"202","author":"R Chapaneri","year":"2022","unstructured":"Chapaneri, R., Shah, S.: Enhanced detection of imbalanced malicious network traffic with regularized generative adversarial networks. J. Netw. Comput. Appl. 202, 103368 (2022)","journal-title":"J. Netw. Comput. Appl."},{"key":"3_CR5","unstructured":"Gulrajani, I., Ahmed, F., Arjovsky, M., Dumoulin, V., Courville, A.: Improved training of wasserstein GANs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, NIPS 2017, pp. 5769\u20135779. Curran Associates Inc (2017)"},{"key":"3_CR6","doi-asserted-by":"crossref","unstructured":"Hao, X., et al.: Producing more with less: a gan-based network attack detection approach for imbalanced data. In: 2021 IEEE 24th International Conference on Computer Supported Cooperative Work in Design (CSCWD), pp. 384\u2013390. IEEE (2021)","DOI":"10.1109\/CSCWD49262.2021.9437863"},{"issue":"1","key":"3_CR7","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s40537-019-0192-5","volume":"6","author":"JM Johnson","year":"2019","unstructured":"Johnson, J.M., Khoshgoftaar, T.M.: Survey on deep learning with class imbalance. J. Big Data 6(1), 1\u201354 (2019)","journal-title":"J. Big Data"},{"key":"3_CR8","doi-asserted-by":"publisher","DOI":"10.1016\/j.cose.2022.103054","volume":"125","author":"V Kumar","year":"2023","unstructured":"Kumar, V., Sinha, D.: Synthetic attack data generation model applying generative adversarial network for intrusion detection. Comput. Secur. 125, 103054 (2023)","journal-title":"Comput. Secur."},{"key":"3_CR9","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1016\/j.neucom.2016.12.038","volume":"234","author":"W Liu","year":"2017","unstructured":"Liu, W., Wang, Z., Liu, X., Zeng, N., Liu, Y., Alsaadi, F.E.: A survey of deep neural network architectures and their applications. Neurocomputing 234, 11\u201326 (2017)","journal-title":"Neurocomputing"},{"key":"3_CR10","doi-asserted-by":"crossref","unstructured":"Moualla, S., Khorzom, K., Jafar, A.: Improving the performance of machine learning-based network intrusion detection systems on the UNSW-NB15 dataset. Comput. Intell. Neurosci. 2021, e5557577 (2021)","DOI":"10.1155\/2021\/5557577"},{"key":"3_CR11","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), pp. 1\u20136 (2015)","DOI":"10.1109\/MilCIS.2015.7348942"},{"key":"3_CR12","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"220","DOI":"10.1007\/978-3-319-59650-1_19","volume-title":"Hybrid Artificial Intelligent Systems","author":"FJ Pulgar","year":"2017","unstructured":"Pulgar, F.J., Rivera, A.J., Charte, F., del Jesus, M.J.: On the impact of imbalanced data in\u00a0convolutional neural networks performance. In: Mart\u00ednez de Pis\u00f3n, F.J., Urraca, R., Quinti\u00e1n, H., Corchado, E. (eds.) HAIS 2017. LNCS (LNAI), vol. 10334, pp. 220\u2013232. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-59650-1_19"},{"key":"3_CR13","doi-asserted-by":"crossref","unstructured":"Sommer, R., Paxson, V.: Outside the closed world: on using machine learning for network intrusion detection. In: 2010 IEEE Symposium on Security and Privacy, pp. 305\u2013316. IEEE (2010)","DOI":"10.1109\/SP.2010.25"},{"key":"3_CR14","doi-asserted-by":"crossref","unstructured":"Vu, L., Van Tra, D., Nguyen, Q.U.: Learning from imbalanced data for encrypted traffic identification problem. In: Proceedings of the 7th Symposium on Information and Communication Technology, pp. 147\u2013152 (2016)","DOI":"10.1145\/3011077.3011132"},{"issue":"14","key":"3_CR15","doi-asserted-by":"publisher","first-page":"5243","DOI":"10.3390\/s22145243","volume":"22","author":"J Wang","year":"2022","unstructured":"Wang, J., Yan, X., Liu, L., Li, L., Yu, Y.: CTTGAN: traffic data synthesizing scheme based on conditional GAN. Sensors 22(14), 5243 (2022)","journal-title":"Sensors"},{"issue":"4","key":"3_CR16","doi-asserted-by":"publisher","first-page":"2079","DOI":"10.3390\/app13042079","volume":"13","author":"B Xuan","year":"2023","unstructured":"Xuan, B., Li, J., Song, Y.: SFCWGAN-BITCN with sequential features for malware detection. Appl. Sci. 13(4), 2079 (2023)","journal-title":"Appl. Sci."},{"key":"3_CR17","doi-asserted-by":"crossref","unstructured":"Yilmaz, I., Masum, R., Siraj, A.: Addressing imbalanced data problem with generative adversarial network for intrusion detection. In: 2020 IEEE 21st International Conference on Information Reuse and Integration for Data Science (IRI), pp. 25\u201330. IEEE (2020)","DOI":"10.1109\/IRI49571.2020.00012"},{"issue":"12","key":"3_CR18","doi-asserted-by":"publisher","first-page":"1330","DOI":"10.3897\/jucs.85703","volume":"28","author":"M Zekan","year":"2022","unstructured":"Zekan, M., Tomi\u010di\u0107, I., Schatten, M.: Low-sample classification in NIDS using the EC-GAN method. JUCS J. Univ. Comput. Sci. 28(12), 1330\u20131346 (2022)","journal-title":"JUCS J. Univ. Comput. Sci."}],"container-title":["Lecture Notes in Computer Science","Mobile, Secure, and Programmable Networking"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-52426-4_3","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,24]],"date-time":"2024-01-24T06:03:01Z","timestamp":1706076181000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-52426-4_3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031524257","9783031524264"],"references-count":18,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-52426-4_3","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"25 January 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"MSPN","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Mobile, Secure, and Programmable Networking","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Paris","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"France","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 October 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 October 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"9","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"mspn2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/mspn2023.roc.cnam.fr\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"easychair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"31","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"15","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"48% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}