{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,10]],"date-time":"2026-01-10T01:40:32Z","timestamp":1768009232163,"version":"3.49.0"},"reference-count":35,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2025,2,8]],"date-time":"2025-02-08T00:00:00Z","timestamp":1738972800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,2,8]],"date-time":"2025-02-08T00:00:00Z","timestamp":1738972800000},"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\/s42979-025-03656-4","type":"journal-article","created":{"date-parts":[[2025,2,8]],"date-time":"2025-02-08T11:06:53Z","timestamp":1739012813000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["TLBS-IDS: A New Hybrid Network Intrusion Detection System for Imbalanced Dataset in a Higher Education Setting"],"prefix":"10.1007","volume":"6","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6989-5303","authenticated-orcid":false,"given":"Hanane","family":"Chliah","sequence":"first","affiliation":[]},{"given":"Maryem","family":"Ait El Hadj","sequence":"additional","affiliation":[]},{"given":"Amal","family":"Battou","sequence":"additional","affiliation":[]},{"given":"Adil","family":"Laoufi","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,2,8]]},"reference":[{"key":"3656_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cose.2019.101568","volume":"87","author":"L Connolly","year":"2019","unstructured":"Connolly L, Wall DS. The rise of crypto-ransomware in a changing cybercrime landscape: taxonomising countermeasures. Comput Secur. 2019;87: 101568. https:\/\/doi.org\/10.1016\/j.cose.2019.101568.","journal-title":"Comput Secur"},{"key":"3656_CR2","doi-asserted-by":"publisher","DOI":"10.3390\/app11135841","author":"E Kristen","year":"2021","unstructured":"Kristen E, Kloibhofer R, D\u00edaz VH, Castillejo P. Security assessment of agriculture IoT (AIoT) applications. Appl Sci. 2021. https:\/\/doi.org\/10.3390\/app11135841.","journal-title":"Appl Sci"},{"key":"3656_CR3","doi-asserted-by":"publisher","DOI":"10.3390\/fi12100167","author":"N Thapa","year":"2020","unstructured":"Thapa N, Liu Z, Dukka B KC, Gokaraju B, Roy K. Comparison of machine learning and deep learning models for network intrusion detection systems. Future Internet. 2020. https:\/\/doi.org\/10.3390\/fi12100167.","journal-title":"Future Internet."},{"key":"3656_CR4","doi-asserted-by":"publisher","unstructured":"Spelmen VS, Porkodi R. A review on handling imbalanced data. In: 2018 International conference on current trends towards converging technologies (ICCTCT). 2018. pp 1\u201311. https:\/\/doi.org\/10.1109\/ICCTCT.2018.8551020","DOI":"10.1109\/ICCTCT.2018.8551020"},{"issue":"6","key":"3656_CR5","doi-asserted-by":"publisher","first-page":"4076","DOI":"10.1002\/ett.4076","volume":"32","author":"BS Bhati","year":"2021","unstructured":"Bhati BS, Chugh G, Al-Turjman F, Bhati NS. An improved ensemble based intrusion detection technique using XGBoost. Trans Emerg Telecommun Technol. 2021;32(6):4076. https:\/\/doi.org\/10.1002\/ett.4076.","journal-title":"Trans Emerg Telecommun Technol"},{"key":"3656_CR6","unstructured":"Goswami S. Class imbalance: SMOTE, borderline-SMOTE, and ADASYN. 2024. https:\/\/towardsdatascience.com\/class-imbalance-smote-borderline-smote-adasyn-6e36c78d804. Accessed 04 Aug 2024"},{"issue":"5","key":"3656_CR7","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. The class imbalance problem: a systematic study. Intell Data Anal. 2002;6(5):429\u201349.","journal-title":"Intell Data Anal"},{"issue":"2","key":"3656_CR8","doi-asserted-by":"publisher","first-page":"493","DOI":"10.1007\/s12083-017-0630-0","volume":"12","author":"N Sultana","year":"2019","unstructured":"Sultana N, Chilamkurti N, Peng W, Alhadad R. Survey on SDN based network intrusion detection system using machine learning approaches. Peer-to-Peer Netw Appl. 2019;12(2):493\u2013501. https:\/\/doi.org\/10.1007\/s12083-017-0630-0.","journal-title":"Peer-to-Peer Netw Appl"},{"issue":"1","key":"3656_CR9","doi-asserted-by":"publisher","first-page":"131","DOI":"10.1007\/s12652-022-04393-9","volume":"14","author":"O AbuAlghanam","year":"2023","unstructured":"AbuAlghanam O, Alazzam H, Alhenawi E, Qatawneh M, Adwan O. Fusion-based anomaly detection system using modified isolation forest for internet of things. J Ambient Intell Human Comput. 2023;14(1):131\u201345. https:\/\/doi.org\/10.1007\/s12652-022-04393-9.","journal-title":"J Ambient Intell Human Comput"},{"key":"3656_CR10","doi-asserted-by":"publisher","unstructured":"Lippmann RP, Fried DJ, Graf I, Haines JW, Kendall KR, McClung D, Weber D, Webster SE, Wyschogrod D, Cunningham RK, Zissman MA. Evaluating intrusion detection systems: the 1998 DARPA off-line intrusion detection evaluation. In: Proceedings DARPA information survivability conference and exposition. DISCEX\u201900, vol. 2. 2000. pp. 12\u2013262. https:\/\/doi.org\/10.1109\/DISCEX.2000.821506","DOI":"10.1109\/DISCEX.2000.821506"},{"key":"3656_CR11","unstructured":"KDD Cup 1999 Data. https:\/\/kdd.ics.uci.edu\/databases\/kddcup99\/kddcup99.html. Accessed 04 Aug 2024."},{"key":"3656_CR12","unstructured":"University of New Brunswick. NSL-KDD dataset. 2009. https:\/\/www.unb.ca\/cic\/datasets\/nsl.html."},{"key":"3656_CR13","doi-asserted-by":"publisher","DOI":"10.1155\/2021\/5557577","author":"S Moualla","year":"2021","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. https:\/\/doi.org\/10.1155\/2021\/5557577.","journal-title":"Comput Intell Neurosci"},{"key":"3656_CR14","doi-asserted-by":"publisher","unstructured":"Carneiro J, Oliveira N, Sousa N, Maia E, Pra\u00e7a I. Machine learning for network-based intrusion detection systems: an analysis of the CIDDS-001 dataset. In: International symposium on distributed computing and artificial intelligence. 2021. pp. 148\u2013158. https:\/\/doi.org\/10.1007\/978-3-030-86261-9_15. Springer","DOI":"10.1007\/978-3-030-86261-9_15"},{"issue":"2","key":"3656_CR15","doi-asserted-by":"publisher","first-page":"1133","DOI":"10.1007\/s10489-020-01886-y","volume":"51","author":"P Bedi","year":"2021","unstructured":"Bedi P, Gupta N, Jindal V. I-SiamIDS: an improved Siam-IDS for handling class imbalance in network-based intrusion detection systems. Appl Intell. 2021;51(2):1133\u201351. https:\/\/doi.org\/10.1007\/s10489-020-01886-y.","journal-title":"Appl Intell"},{"key":"3656_CR16","doi-asserted-by":"publisher","DOI":"10.14569\/IJACSA.2020.0110670","author":"MA Albahar","year":"2020","unstructured":"Albahar MA, Binsawad M, Almalki J, El-etriby S, Karali S. Improving intrusion detection system using artificial neural network. Int J Adv Comput Sci Appl. 2020. https:\/\/doi.org\/10.14569\/IJACSA.2020.0110670.","journal-title":"Int J Adv Comput Sci Appl"},{"key":"3656_CR17","doi-asserted-by":"publisher","DOI":"10.1016\/j.comnet.2020.107315","volume":"177","author":"H Zhang","year":"2020","unstructured":"Zhang H, Huang L, Wu CQ, Li Z. An effective convolutional neural network based on SMOTE and gaussian mixture model for intrusion detection in imbalanced dataset. Comput Netw. 2020;177: 107315. https:\/\/doi.org\/10.1016\/j.comnet.2020.107315.","journal-title":"Comput Netw"},{"issue":"1","key":"3656_CR18","doi-asserted-by":"publisher","first-page":"297","DOI":"10.32604\/iasc.2023.026799","volume":"35","author":"R Almarshdi","year":"2023","unstructured":"Almarshdi R, Nassef L, Fadel E, Alowidi N. Hybrid deep learning based attack detection for imbalanced data classification. Intell Autom Soft Comput. 2023;35(1):297\u2013320. https:\/\/doi.org\/10.32604\/iasc.2023.026799.","journal-title":"Intell Autom Soft Comput"},{"key":"3656_CR19","doi-asserted-by":"publisher","first-page":"70245","DOI":"10.1109\/ACCESS.2020.2986882","volume":"8","author":"A Kim","year":"2020","unstructured":"Kim A, Park M, Lee DH. AI-IDS: application of deep learning to real-time web intrusion detection. IEEE Access. 2020;8:70245\u201361. https:\/\/doi.org\/10.1109\/ACCESS.2020.2986882.","journal-title":"IEEE Access"},{"key":"3656_CR20","doi-asserted-by":"publisher","first-page":"126646","DOI":"10.1109\/ACCESS.2021.3111053","volume":"9","author":"V Christopher","year":"2021","unstructured":"Christopher V, Aathman T, Mahendrakumaran K, Nawaratne R, De Silva D, Nanayakkara V, Alahakoon D. Minority resampling boosted unsupervised learning with hyperdimensional computing for threat detection at the edge of internet of things. IEEE Access. 2021;9:126646\u201357. https:\/\/doi.org\/10.1109\/ACCESS.2021.3111053.","journal-title":"IEEE Access"},{"key":"3656_CR21","doi-asserted-by":"publisher","unstructured":"Abushwereb M, Alkasassbeh M, Almseidin M, Mustafa MK. an accurate IoT intrusion detection framework using apache spark. 2022. arXiv:2203.04347. https:\/\/doi.org\/10.48550\/arXiv.2203.04347","DOI":"10.48550\/arXiv.2203.04347"},{"key":"3656_CR22","doi-asserted-by":"publisher","unstructured":"Tavallaee M, Bagheri E, Lu W, Ghorbani AA. A detailed analysis of the KDD CUP 99 data set. In: 2009 IEEE symposium on computational intelligence for security and defense applications. 2009. pp 1\u20136. https:\/\/doi.org\/10.1109\/CISDA.2009.5356528.","DOI":"10.1109\/CISDA.2009.5356528"},{"issue":"4","key":"3656_CR23","doi-asserted-by":"publisher","first-page":"262","DOI":"10.1145\/382912.382923","volume":"3","author":"J McHugh","year":"2000","unstructured":"McHugh J. Testing intrusion detection systems: a critique of the 1998 and 1999 DARPA intrusion detection system evaluations as performed by Lincoln laboratory. ACM Trans Inf Syst Secur. 2000;3(4):262\u201394. https:\/\/doi.org\/10.1145\/382912.382923.","journal-title":"ACM Trans Inf Syst Secur"},{"key":"3656_CR24","doi-asserted-by":"publisher","DOI":"10.2139\/ssrn.3428211","author":"RD Ravipati","year":"2019","unstructured":"Ravipati RD, Abualkibash M. Intrusion detection system classification using different machine learning algorithms on KDD-99 and NSL-KDD datasets: a review paper. Int J Comput Sci Inf Technol (IJCSIT). 2019. https:\/\/doi.org\/10.2139\/ssrn.3428211.","journal-title":"Int J Comput Sci Inf Technol (IJCSIT)"},{"key":"3656_CR25","doi-asserted-by":"crossref","unstructured":"Claise B. Cisco systems NetFlow services export version 9. Technical report, Cisco Systems. 2004. https:\/\/www.rfc-editor.org\/rfc\/rfc3954.","DOI":"10.17487\/rfc3954"},{"key":"3656_CR26","doi-asserted-by":"publisher","first-page":"80716","DOI":"10.1109\/ACCESS.2020.2988796","volume":"8","author":"KP Sinaga","year":"2020","unstructured":"Sinaga KP, Yang M-S. Unsupervised K-means clustering algorithm. IEEE Access. 2020;8:80716\u201327. https:\/\/doi.org\/10.1109\/ACCESS.2020.2988796.","journal-title":"IEEE Access"},{"key":"3656_CR27","doi-asserted-by":"publisher","first-page":"141","DOI":"10.1023\/A:1009783824328","volume":"1","author":"T Zhang","year":"1997","unstructured":"Zhang T, Ramakrishnan R, Livny M. BIRCH: a new data clustering algorithm and its applications. Data Min Knowl Discov. 1997;1:141\u201382. https:\/\/doi.org\/10.1023\/A:1009783824328.","journal-title":"Data Min Knowl Discov"},{"key":"3656_CR28","doi-asserted-by":"publisher","unstructured":"Ai-jun L, Peng Z. Research on unbalanced data processing algorithm base Tomeklinks-Smote. In: Proceedings of the 2020 3rd international conference on artificial intelligence and pattern recognition, AIPR \u201920. Association for Computing Machinery, New York, NY, USA. 2020. pp. 13\u201317. https:\/\/doi.org\/10.1145\/3430199.3430222.","DOI":"10.1145\/3430199.3430222"},{"key":"3656_CR29","doi-asserted-by":"publisher","first-page":"878","DOI":"10.1007\/11538059_91","volume-title":"Advances in intelligent computing","author":"H Han","year":"2005","unstructured":"Han H, Wang W-Y, Mao B-H. Borderline-SMOTE: a new over-sampling method in imbalanced data sets learning. In: Huang D-S, Zhang X-P, Huang G-B, editors. Advances in intelligent computing. Berlin, Heidelberg: Springer; 2005. p. 878\u201387. https:\/\/doi.org\/10.1007\/11538059_91."},{"key":"3656_CR30","doi-asserted-by":"publisher","unstructured":"O\u2019Shea K, Nash R. An introduction to convolutional neural networks. 2015. arXiv preprint arXiv:1511.08458, https:\/\/doi.org\/10.48550\/arXiv.1511.08458.","DOI":"10.48550\/arXiv.1511.08458"},{"key":"3656_CR31","doi-asserted-by":"publisher","unstructured":"Albawi S, Mohammed TA, Al-Zawi S. Understanding of a convolutional neural network. In: 2017 International conference on engineering and technology (ICET). 2017. pp. 1\u20136. https:\/\/doi.org\/10.1109\/ICEngTechnol.2017.8308186.","DOI":"10.1109\/ICEngTechnol.2017.8308186"},{"key":"3656_CR32","doi-asserted-by":"publisher","DOI":"10.1016\/j.physd.2019.132306","volume":"404","author":"A Sherstinsky","year":"2020","unstructured":"Sherstinsky A. Fundamentals of recurrent neural network (RNN) and long short-term memory (LSTM) network. Physica D Nonlinear Phenom. 2020;404: 132306. https:\/\/doi.org\/10.1016\/j.physd.2019.132306.","journal-title":"Physica D Nonlinear Phenom"},{"key":"3656_CR33","doi-asserted-by":"publisher","unstructured":"Staudemeyer RC, Morris ER. Understanding LSTM\u2014a tutorial into long short-term memory recurrent neural networks. 2019. arXiv preprint arXiv:1909.09586, https:\/\/doi.org\/10.48550\/arXiv.1909.09586.","DOI":"10.48550\/arXiv.1909.09586"},{"key":"3656_CR34","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/978-981-15-1209-4_1","volume-title":"Knowledge and systems sciences","author":"D-T Dinh","year":"2019","unstructured":"Dinh D-T, Fujinami T, Huynh V-N. Estimating the optimal number of clusters in categorical data clustering by silhouette coefficient. In: Chen J, Huynh VN, Nguyen G-N, Tang X, editors. Knowledge and systems sciences. Singapore: Springer; 2019. p. 1\u201317. https:\/\/doi.org\/10.1007\/978-981-15-1209-4_1."},{"key":"3656_CR35","unstructured":"Petrovic S. A comparison between the silhouette index and the Davies\u2013Bouldin index in labelling ids clusters. In: Proceedings of the 11th Nordic workshop of secure IT systems, vol. 2006. 2006. pp. 53\u201364."}],"container-title":["SN Computer Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s42979-025-03656-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s42979-025-03656-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s42979-025-03656-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,3,17]],"date-time":"2025-03-17T05:30:10Z","timestamp":1742189410000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s42979-025-03656-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,2,8]]},"references-count":35,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2025,2]]}},"alternative-id":["3656"],"URL":"https:\/\/doi.org\/10.1007\/s42979-025-03656-4","relation":{},"ISSN":["2661-8907"],"issn-type":[{"value":"2661-8907","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,2,8]]},"assertion":[{"value":"6 April 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 January 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 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":"None.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"149"}}