{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,22]],"date-time":"2026-07-22T03:06:38Z","timestamp":1784689598870,"version":"3.55.0"},"reference-count":42,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Journal of Network and Computer Applications"],"published-print":{"date-parts":[[2026,5]]},"DOI":"10.1016\/j.jnca.2026.104461","type":"journal-article","created":{"date-parts":[[2026,2,25]],"date-time":"2026-02-25T16:19:43Z","timestamp":1772036383000},"page":"104461","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":3,"special_numbering":"C","title":["Enhancing real-time IoT intrusion detection using KAN-based frameworks with SMOTE"],"prefix":"10.1016","volume":"249","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4326-0120","authenticated-orcid":false,"given":"Ahmed Burhan","family":"Mohammed","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1484-5069","authenticated-orcid":false,"given":"Ekram","family":"Chamseddine","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8879-6821","authenticated-orcid":false,"given":"Asma","family":"ElAdel","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.jnca.2026.104461_b1","doi-asserted-by":"crossref","first-page":"134837","DOI":"10.1109\/ACCESS.2024.3462297","article-title":"CKAN: Convolutional Kolmogorov\u2013Arnold networks model for intrusion detection in IoT environment","volume":"12","author":"Abd Elaziz","year":"2024","journal-title":"IEEE Access"},{"key":"10.1016\/j.jnca.2026.104461_b2","doi-asserted-by":"crossref","first-page":"472","DOI":"10.1016\/j.aej.2024.12.106","article-title":"Enhancing Internet of Things security using performance gradient boosting for network intrusion detection systems","volume":"116","author":"Ahmed","year":"2025","journal-title":"Alex. Eng. J."},{"key":"10.1016\/j.jnca.2026.104461_b3","doi-asserted-by":"crossref","first-page":"20577","DOI":"10.1038\/s41598-025-06363-5","article-title":"Smart deep learning model for enhanced IoT intrusion detection","volume":"15","author":"Alsubaei","year":"2025","journal-title":"Sci. Rep."},{"key":"10.1016\/j.jnca.2026.104461_b4","series-title":"Enhancing intrusion detection in IoT environments: An advanced ensemble approach using Kolmogorov-Arnold networks","author":"Amouri","year":"2024"},{"key":"10.1016\/j.jnca.2026.104461_b5","doi-asserted-by":"crossref","DOI":"10.1016\/j.asoc.2022.109588","article-title":"Handling class imbalance in COVID-19 chest X-ray images classification: Using SMOTE and weighted loss","volume":"129","author":"Chamseddine","year":"2022","journal-title":"Appl. Soft Comput."},{"key":"10.1016\/j.jnca.2026.104461_b6","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1613\/jair.953","article-title":"SMOTE: Synthetic minority over-sampling technique","volume":"16","author":"Chawla","year":"2002","journal-title":"J. Artificial Intelligence Res."},{"issue":"1","key":"10.1016\/j.jnca.2026.104461_b7","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1016\/j.icte.2025.01.005","article-title":"Deep learning-driven methods for network-based intrusion detection systems: A systematic review","volume":"11","author":"Chinnasamy","year":"2025","journal-title":"ICT Express"},{"key":"10.1016\/j.jnca.2026.104461_b8","article-title":"TFKAN: Transformer based on Kolmogorov\u2013Arnold networks for intrusion detection in IoT environment","volume":"30","author":"Fares","year":"2025","journal-title":"Egypt. Inf. J."},{"key":"10.1016\/j.jnca.2026.104461_b9","author":"Fortune Business Insights","year":"2025"},{"key":"10.1016\/j.jnca.2026.104461_b10","article-title":"Improving massive access to IoT gateways","volume":"157\u2013158","author":"Gelenbe","year":"2022","journal-title":"Perform. Eval."},{"key":"10.1016\/j.jnca.2026.104461_b11","doi-asserted-by":"crossref","DOI":"10.1016\/j.ijcip.2025.100768","article-title":"Using Kolmogorov\u2013Arnold network for cyber\u2013physical system security: A fast and efficient approach","volume":"50","author":"Ghorbani","year":"2025","journal-title":"Int. J. Crit. Infrastruct. Prot."},{"key":"10.1016\/j.jnca.2026.104461_b12","doi-asserted-by":"crossref","first-page":"44624","DOI":"10.1038\/s41598-025-32697-1","article-title":"A novel deep learning framework with temporal attention convolutional networks for intrusion detection in IoT and IIoT networks","volume":"15","author":"Ghosh","year":"2025","journal-title":"Sci. Rep."},{"key":"10.1016\/j.jnca.2026.104461_b13","series-title":"Enhancing IoT security with CNN and LSTM-based intrusion detection systems","author":"Gueriani","year":"2024"},{"issue":"4(121)","key":"10.1016\/j.jnca.2026.104461_b14","doi-asserted-by":"crossref","first-page":"56","DOI":"10.15587\/1729-4061.2023.274575","article-title":"Anomaly detection in internet of medical things with artificial intelligence","volume":"1","author":"Hussein","year":"2023","journal-title":"Eastern-European J. Enterp. Technol."},{"issue":"22","key":"10.1016\/j.jnca.2026.104461_b15","doi-asserted-by":"crossref","DOI":"10.3390\/app142210173","article-title":"How resilient are Kolmogorov\u2013Arnold networks in classification tasks? A robustness investigation","volume":"14","author":"Ibrahum","year":"2024","journal-title":"Appl. Sci."},{"key":"10.1016\/j.jnca.2026.104461_b16","series-title":"A comprehensive survey on Kolmogorov Arnold networks (KAN)","author":"Ji","year":"2025"},{"key":"10.1016\/j.jnca.2026.104461_b17","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2024.107358","article-title":"KansNet: Kolmogorov\u2013Arnold networks and multi slice partition channel priority attention in convolutional neural network for lung nodule detection","volume":"103","author":"Jiang","year":"2025","journal-title":"Biomed. Signal Process. Control."},{"key":"10.1016\/j.jnca.2026.104461_b18","doi-asserted-by":"crossref","DOI":"10.1016\/j.jnca.2023.103760","article-title":"Deep transfer learning for intrusion detection in industrial control networks: A comprehensive review","volume":"220","author":"Kheddar","year":"2023","journal-title":"J. Netw. Comput. Appl."},{"key":"10.1016\/j.jnca.2026.104461_b19","series-title":"2024 24th International Conference on Control, Automation and Systems","first-page":"958","article-title":"Kolmogorov-Arnold networks for online reinforcement learning","author":"Kich","year":"2024"},{"issue":"18","key":"10.1016\/j.jnca.2026.104461_b20","doi-asserted-by":"crossref","DOI":"10.3390\/electronics13183601","article-title":"Machine learning-based intrusion detection methods in IoT systems: A comprehensive review","volume":"13","author":"Kikissagbe","year":"2024","journal-title":"Electronics"},{"key":"10.1016\/j.jnca.2026.104461_b21","doi-asserted-by":"crossref","DOI":"10.1016\/j.cma.2024.117397","article-title":"KAN-odes: Kolmogorov\u2013Arnold network ordinary differential equations for learning dynamical systems and hidden physics","volume":"432","author":"Koenig","year":"2024","journal-title":"Comput. Methods Appl. Mech. Engrg."},{"key":"10.1016\/j.jnca.2026.104461_b22","series-title":"KAN: Kolmogorov-Arnold networks","author":"Liu","year":"2025"},{"key":"10.1016\/j.jnca.2026.104461_b23","series-title":"Risks and Security of Internet and Systems. CRiSIS 2022","first-page":"37","article-title":"A comparative study of attribute selection algorithms on intrusion detection system in UAVs: A case study of UKM-IDS20 dataset","volume":"vol. 13857","author":"Mohammed","year":"2023"},{"key":"10.1016\/j.jnca.2026.104461_b24","doi-asserted-by":"crossref","DOI":"10.1016\/j.comnet.2023.110140","article-title":"Comprehensive systematic review of intelligent approaches in UAV-based intrusion detection, blockchain, and network security","volume":"239","author":"Mohammed","year":"2024","journal-title":"Comput. Netw."},{"key":"10.1016\/j.jnca.2026.104461_b25","series-title":"KANs for computer vision: An experimental study","author":"Mohan","year":"2024"},{"issue":"2","key":"10.1016\/j.jnca.2026.104461_b26","doi-asserted-by":"crossref","DOI":"10.3390\/s22020432","article-title":"Realguard: A lightweight network intrusion detection system for IoT gateways","volume":"22","author":"Nguyen","year":"2022","journal-title":"Sensors"},{"key":"10.1016\/j.jnca.2026.104461_b27","doi-asserted-by":"crossref","DOI":"10.1016\/j.jnca.2023.103637","article-title":"Intelligent approaches toward intrusion detection systems for Industrial Internet of Things: A systematic comprehensive review","volume":"215","author":"Nuaimi","year":"2023","journal-title":"J. Netw. Comput. Appl."},{"key":"10.1016\/j.jnca.2026.104461_b28","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s42400-023-00178-5","article-title":"Quantized autoencoder (QAE) intrusion detection system for anomaly detection in resource-constrained IoT devices using RT-IoT2022 dataset","volume":"6","author":"Sharmila","year":"2023","journal-title":"Cybersecurity"},{"key":"10.1016\/j.jnca.2026.104461_b29","doi-asserted-by":"crossref","DOI":"10.1016\/j.asoc.2023.111080","article-title":"Deep Q-network-based heuristic intrusion detection against edge-based SIoT zero-day attacks","volume":"150","author":"Shen","year":"2024","journal-title":"Appl. Soft Comput."},{"issue":"1","key":"10.1016\/j.jnca.2026.104461_b30","doi-asserted-by":"crossref","first-page":"9684","DOI":"10.1038\/s41598-025-94500-5","article-title":"A high performance hybrid LSTM CNN secure architecture for IoT environments using deep learning","volume":"15","author":"Sinha","year":"2025","journal-title":"Sci. Rep."},{"key":"10.1016\/j.jnca.2026.104461_b31","article-title":"A survey on Kolmogorov-Arnold network","author":"Somvanshi","year":"2025","journal-title":"ACM Comput. Surv."},{"key":"10.1016\/j.jnca.2026.104461_b32","doi-asserted-by":"crossref","DOI":"10.1016\/j.jnca.2023.103761","article-title":"NLP methods in host-based intrusion detection systems: A systematic review and future directions","volume":"220","author":"Sworna","year":"2023","journal-title":"J. Netw. Comput. Appl."},{"issue":"4","key":"10.1016\/j.jnca.2026.104461_b33","article-title":"KAN-enhanced deep reinforcement learning for chaos control: Achieving rapid stabilization via minor perturbations","author":"Tongtao Liu","year":"2025","journal-title":"Phys. D: Nonlinear Phenom."},{"key":"10.1016\/j.jnca.2026.104461_b34","series-title":"Attention is all you need","author":"Vaswani","year":"2023"},{"issue":"1","key":"10.1016\/j.jnca.2026.104461_b35","doi-asserted-by":"crossref","first-page":"1917","DOI":"10.1038\/s41598-024-85083-8","article-title":"An intrusion detection model based on Convolutional Kolmogorov-Arnold Networks","volume":"15","author":"Wang","year":"2025","journal-title":"Sci. Rep."},{"issue":"12","key":"10.1016\/j.jnca.2026.104461_b36","doi-asserted-by":"crossref","DOI":"10.1002\/advs.202413805","article-title":"Accurately models the relationship between physical response and structure using Kolmogorov\u2013Arnold network","volume":"12","author":"Wang","year":"2025","journal-title":"Adv. Sci."},{"key":"10.1016\/j.jnca.2026.104461_b37","doi-asserted-by":"crossref","first-page":"8648","DOI":"10.1038\/s41598-025-88054-9","article-title":"Graph attention and Kolmogorov\u2013Arnold network based smart grids intrusion detection","volume":"15","author":"Wu","year":"2025","journal-title":"Sci. Rep."},{"key":"10.1016\/j.jnca.2026.104461_b38","doi-asserted-by":"crossref","first-page":"19339","DOI":"10.1038\/s41598-024-70094-2","article-title":"An improved intrusion detection method for IIoT using attention mechanisms, BiGRU, and Inception-CNN","volume":"14","author":"Yang","year":"2024","journal-title":"Sci. Rep."},{"issue":"4","key":"10.1016\/j.jnca.2026.104461_b39","first-page":"1003","article-title":"Employing hybrid ANOVA-RFE with machine and deep learning models for enhanced IoT and IIoT attack detection and classification","volume":"28","author":"Yassen","year":"2023","journal-title":"Ing. Syst. D\u2019Inf."},{"issue":"4","key":"10.1016\/j.jnca.2026.104461_b40","first-page":"1003","article-title":"Employing hybrid ANOVA-RFE with machine and deep learning models for enhanced IoT and IIoT attack detection and classification","volume":"28","author":"Yassen","year":"2023","journal-title":"Ing. Des Syst. D\u2019Information"},{"issue":"17","key":"10.1016\/j.jnca.2026.104461_b41","doi-asserted-by":"crossref","first-page":"29132","DOI":"10.1109\/JIOT.2024.3406386","article-title":"Novel intrusion detection strategies with optimal hyper parameters for industrial Internet of Things based on stochastic games and double deep Q-networks","volume":"11","author":"Yu","year":"2024","journal-title":"IEEE Internet Things J."},{"issue":"7","key":"10.1016\/j.jnca.2026.104461_b42","doi-asserted-by":"crossref","first-page":"12536","DOI":"10.1109\/JIOT.2023.3333903","article-title":"Deep Q-network-based open-set intrusion detection solution for industrial internet of things","volume":"11","author":"Yu","year":"2024","journal-title":"IEEE Internet Things J."}],"container-title":["Journal of Network and Computer Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1084804526000366?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1084804526000366?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T16:12:45Z","timestamp":1773763965000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1084804526000366"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,5]]},"references-count":42,"alternative-id":["S1084804526000366"],"URL":"https:\/\/doi.org\/10.1016\/j.jnca.2026.104461","relation":{},"ISSN":["1084-8045"],"issn-type":[{"value":"1084-8045","type":"print"}],"subject":[],"published":{"date-parts":[[2026,5]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Enhancing real-time IoT intrusion detection using KAN-based frameworks with SMOTE","name":"articletitle","label":"Article Title"},{"value":"Journal of Network and Computer Applications","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.jnca.2026.104461","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"104461"}}