{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,15]],"date-time":"2026-07-15T16:08:09Z","timestamp":1784131689628,"version":"3.55.0"},"reference-count":21,"publisher":"Springer Science and Business Media LLC","issue":"8","license":[{"start":{"date-parts":[[2024,11,28]],"date-time":"2024-11-28T00:00:00Z","timestamp":1732752000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,11,28]],"date-time":"2024-11-28T00:00:00Z","timestamp":1732752000000},"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":["SN COMPUT. SCI."],"DOI":"10.1007\/s42979-024-03468-y","type":"journal-article","created":{"date-parts":[[2024,11,28]],"date-time":"2024-11-28T12:21:46Z","timestamp":1732796506000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Impact of Class Balancing on Intrusion Detection System for WSN-BFSF Dataset"],"prefix":"10.1007","volume":"5","author":[{"given":"Vaishali","family":"Soni","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6460-8355","authenticated-orcid":false,"given":"Deepika","family":"Kukreja","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Amarjit","family":"Malhotra","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,11,28]]},"reference":[{"issue":"8","key":"3468_CR1","doi-asserted-by":"publisher","first-page":"1707","DOI":"10.1109\/LWC.2021.3077946","volume":"10","author":"R Zhao","year":"2021","unstructured":"Zhao R, Yin J, Xue Z, Gui G, Adebisi B, Ohtsuki T, Gacanin H, Sari H. An efficient intrusion detection method based on dynamic autoencoder. IEEE Wirel Commun Lett. 2021;10(8):1707\u201311. https:\/\/doi.org\/10.1109\/LWC.2021.3077946.","journal-title":"IEEE Wirel Commun Lett"},{"issue":"2\u2013A","key":"3468_CR2","doi-asserted-by":"publisher","first-page":"441","DOI":"10.47974\/JDMSC-1900","volume":"27","author":"M Alate","year":"2024","unstructured":"Alate M, Godase UR, Kumbhar UT, Hundekari S, Deshmukh AA, Kumar A. Secure and scalable data aggregation techniques for healthcare monitoring in wsn. J Discret Math Sci Cryptogr. 2024;27(2\u2013A):441\u201352. https:\/\/doi.org\/10.47974\/JDMSC-1900.","journal-title":"J Discret Math Sci Cryptogr"},{"key":"3468_CR3","doi-asserted-by":"publisher","unstructured":"Le T-T-H, Park T, Cho D, Kim H. An effective classification for dos attacks in wireless sensor networks, 2018. pp 689\u2013692. https:\/\/doi.org\/10.1109\/ICUFN.2018.8436999","DOI":"10.1109\/ICUFN.2018.8436999"},{"key":"3468_CR4","doi-asserted-by":"publisher","DOI":"10.3390\/app10051775","author":"R Mag\u00e1n-Carri\u00f3n","year":"2020","unstructured":"Mag\u00e1n-Carri\u00f3n R, Urda D, D\u00edaz-Cano I, Dorronsoro B. Towards a reliable comparison and evaluation of network intrusion detection systems based on machine learning approaches. Appl Sci. 2020. https:\/\/doi.org\/10.3390\/app10051775.","journal-title":"Appl Sci"},{"key":"3468_CR5","doi-asserted-by":"publisher","DOI":"10.1016\/j.matpr.2021.07.378","author":"P Gite","year":"2021","unstructured":"Gite P, Chouhan K, Krishna K, Nayak C, Soni M, Shrivastava A. Ml based intrusion detection scheme for various types of attacks in a wsn using c4.5 and cart classifiers. Mater Today: Proc. 2021. https:\/\/doi.org\/10.1016\/j.matpr.2021.07.378.","journal-title":"Mater Today: Proc"},{"key":"3468_CR6","doi-asserted-by":"publisher","unstructured":"Mohi-ud-din G: NSL-KDD. https:\/\/doi.org\/10.21227\/425a-3e55","DOI":"10.21227\/425a-3e55"},{"issue":"3","key":"3468_CR7","doi-asserted-by":"publisher","first-page":"12","DOI":"10.1109\/MPRV.2018.03367731","volume":"17","author":"Y Meidan","year":"2018","unstructured":"Meidan Y, Bohadana M, Mathov Y, Mirsky Y, Shabtai A, Breitenbacher D, Elovici Y. N-baiot-network-based detection of iot botnet attacks using deep autoencoders. IEEE Pervasive Comput. 2018;17(3):12\u201322. https:\/\/doi.org\/10.1109\/MPRV.2018.03367731.","journal-title":"IEEE Pervasive Comput"},{"key":"3468_CR8","doi-asserted-by":"crossref","unstructured":"Koroniotis N, Moustafa N, Sitnikova E, Turnbull BP. Towards the development of realistic botnet dataset in the internet of things for network forensic analytics: Bot-iot dataset. CoRR arXiv:1811.00701 (2018)","DOI":"10.1016\/j.future.2019.05.041"},{"key":"3468_CR9","doi-asserted-by":"crossref","unstructured":"Sharafaldin I, Lashkari AH, Ghorbani AA. Toward generating a new intrusion detection dataset and intrusion traffic characterization. In: International Conference on Information Systems Security and Privacy (2018)","DOI":"10.5220\/0006639801080116"},{"key":"3468_CR10","doi-asserted-by":"publisher","DOI":"10.1016\/j.scs.2021.102994","volume":"72","author":"N Moustafa","year":"2021","unstructured":"Moustafa N. A new distributed architecture for evaluating ai-based security systems at the edge: network ton-iot datasets. Sustain Cities Soc. 2021;72: 102994. https:\/\/doi.org\/10.1016\/j.scs.2021.102994.","journal-title":"Sustain Cities Soc"},{"key":"3468_CR11","doi-asserted-by":"publisher","DOI":"10.1016\/j.iot.2024.101162","volume":"26","author":"MM Inuwa","year":"2024","unstructured":"Inuwa MM, Das R. A comparative analysis of various machine learning methods for anomaly detection in cyber attacks on iot networks. Internet Things. 2024;26: 101162. https:\/\/doi.org\/10.1016\/j.iot.2024.101162.","journal-title":"Internet Things"},{"issue":"2","key":"3468_CR12","doi-asserted-by":"publisher","first-page":"2109","DOI":"10.1109\/JIOT.2023.3292209","volume":"11","author":"M Dener","year":"2024","unstructured":"Dener M, Okur C, Al S, Orman A. Wsn-bfsf: a new data set for attacks detection in wireless sensor networks. IEEE Internet Things J. 2024;11(2):2109\u201325. https:\/\/doi.org\/10.1109\/JIOT.2023.3292209.","journal-title":"IEEE Internet Things J"},{"key":"3468_CR13","doi-asserted-by":"publisher","first-page":"392","DOI":"10.4314\/njt.v35i2.21","volume":"35","author":"V Ekong","year":"2016","unstructured":"Ekong V, Ekong U. A survey of security vulnerabilities in wireless sensor networks. Niger J Technol. 2016;35:392. https:\/\/doi.org\/10.4314\/njt.v35i2.21.","journal-title":"Niger J Technol"},{"key":"3468_CR14","doi-asserted-by":"publisher","DOI":"10.1088\/1742-6596\/1743\/1\/012021","volume":"1743","author":"S Ifzarne","year":"2021","unstructured":"Ifzarne S, Tabbaa H, Imad H, Lamghari N. Anomaly detection using machine learning techniques in wireless sensor networks. J Phys: Conf Ser. 2021;1743: 012021. https:\/\/doi.org\/10.1088\/1742-6596\/1743\/1\/012021.","journal-title":"J Phys: Conf Ser"},{"key":"3468_CR15","doi-asserted-by":"crossref","unstructured":"Tabbaa H, Ifzarne S, Hafidi I. An online ensemble learning model for detecting attacks in wireless sensor networks, 2022.","DOI":"10.1007\/978-3-031-29313-9_24"},{"key":"3468_CR16","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1186\/s40537-023-00692-w","volume":"10","author":"S Salim","year":"2023","unstructured":"Salim S, Oughdir L. Performance evaluation of deep learning techniques for dos attacks detection in wireless sensor network. J Big Data. 2023;10:17. https:\/\/doi.org\/10.1186\/s40537-023-00692-w.","journal-title":"J Big Data"},{"key":"3468_CR17","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2016\/4731953","volume":"2016","author":"I Almomani","year":"2016","unstructured":"Almomani I, Kasasbeh B, AL-Akhras M. Wsn-ds: a dataset for intrusion detection systems in wireless sensor networks. J Sens. 2016;2016:1\u201316. https:\/\/doi.org\/10.1155\/2016\/4731953.","journal-title":"J Sens"},{"key":"3468_CR18","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2895334","author":"V Ravi","year":"2019","unstructured":"Ravi V, Alazab M, Kp S, Poornachandran P, Al-Nemrat A, Venkatraman S. Deep learning approach for intelligent intrusion detection system. IEEE Access. 2019. https:\/\/doi.org\/10.1109\/ACCESS.2019.2895334.","journal-title":"IEEE Access"},{"key":"3468_CR19","doi-asserted-by":"publisher","unstructured":"Moustafa N, Slay J. Unsw-nb15: a comprehensive data set for network intrusion detection systems (unsw-nb15 network data set). 2015. https:\/\/doi.org\/10.1109\/MilCIS.2015.7348942","DOI":"10.1109\/MilCIS.2015.7348942"},{"key":"3468_CR20","doi-asserted-by":"publisher","first-page":"3339","DOI":"10.3390\/s24113339","volume":"24","author":"T Nguyen","year":"2024","unstructured":"Nguyen T, Vo H, Yoo M. Enhancing intrusion detection in wireless sensor networks using a gswo-catboost approach. Sensors. 2024;24:3339. https:\/\/doi.org\/10.3390\/s24113339.","journal-title":"Sensors"},{"key":"3468_CR21","unstructured":"Google Colaboratory (CoLab). https:\/\/research.google.com\/colaboratory\/faq.html. Accessed 24 May 2024."}],"container-title":["SN Computer Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s42979-024-03468-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s42979-024-03468-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s42979-024-03468-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,28]],"date-time":"2024-11-28T13:08:26Z","timestamp":1732799306000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s42979-024-03468-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,28]]},"references-count":21,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["3468"],"URL":"https:\/\/doi.org\/10.1007\/s42979-024-03468-y","relation":{},"ISSN":["2661-8907"],"issn-type":[{"value":"2661-8907","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,11,28]]},"assertion":[{"value":"30 May 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 October 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 November 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Authors declare that they have no Conflict of interest that could have influenced the results or discussions presented in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"We comply with Ethical Standards.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical Standards"}},{"value":"This research study did not involve the use of human participants or animals. Therefore, ethical approval was not required for this study.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Research Involving Human Participants and\/or Animals"}}],"article-number":"1105"}}