{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T00:39:29Z","timestamp":1777682369383,"version":"3.51.4"},"reference-count":24,"publisher":"SAGE Publications","issue":"3","license":[{"start":{"date-parts":[[2025,5,15]],"date-time":"2025-05-15T00:00:00Z","timestamp":1747267200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Journal of High Speed Networks"],"published-print":{"date-parts":[[2025,8]]},"abstract":"<jats:p>Cybersecurity has increased on software-defined networks (SDNs) since its inception in the early 2010s. That is due to the SDN's centralized nature in controlling the network, human-centered generative methods, and transferring data in the internet of behavior. The widespread usage of Internet of Things (IoT) networks, integrating SDNs with IoT, the intelligent industrial revolution, and the inclusion of the human-centric industrial revolution in the infrastructure of major industrial fields all were reasons to focus on creating effective attack detection systems intrusion detection system (IDS) for these industrial IoT (IIoT) networks. In this paper, we present an IDS we call SPAARC_DDSDN_IIoT. It is a four-layer industrial IoT architecture weaponized by security devices at each layer and uses the SDN's centralized approach. First is an application layer-based prediction approach called SPAARC. SPAARC is a split-point algorithm combined with an attribute-reduced classifier (SPAARC). It is applied to the proposed IDS-based SDN approach in the IIoT. SPAARC is a decision tree algorithm, and intrusion detection will be performed on the leaves of the resulting tree. Two datasets were used for the experiment: the DDoS_SDN and the XIIoT_ID. The firefly algorithm, as a swarm intelligence based on the behavior of fireflies, was used for feature selection. SPAARC achieved a notable accuracy of 99.9962% and 99.991%, surpassing all the other machine learning algorithms tested. SPAARC also achieved a mean absolute error of 0 and 0.008, a root mean squared error of 0.0062 and 0.0016, and a perfect score on both datasets for the remaining metrics.<\/jats:p>","DOI":"10.1177\/09266801251338138","type":"journal-article","created":{"date-parts":[[2025,5,15]],"date-time":"2025-05-15T03:39:35Z","timestamp":1747280375000},"page":"228-243","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":6,"title":["A hybrid firefly-based attribute selection and split-point mechanism for securing software-defined industrial Internet of Things"],"prefix":"10.1177","volume":"31","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-2534-7435","authenticated-orcid":false,"given":"Georg","family":"Thamer Francis","sequence":"first","affiliation":[{"name":"Department of Computer Engineering, Hali\u00e7 University, Istanbul, Turkey"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8314-9051","authenticated-orcid":false,"given":"Alireza","family":"Souri","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Hali\u00e7 University, Istanbul, Turkey"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2989-6632","authenticated-orcid":false,"given":"Nihat","family":"\u0130nan\u00e7","sequence":"additional","affiliation":[{"name":"Department of Electrical and Electronics Engineering, Faculty of Engineering, Hali\u00e7 University, Istanbul, Turkey"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2025,5,15]]},"reference":[{"key":"e_1_3_3_2_2","doi-asserted-by":"publisher","DOI":"10.1109\/SSCI47803.2020.9308293"},{"key":"e_1_3_3_3_2","article-title":"Software defined network (SDN) based Internet of Things (IoT): a road ahead","author":"Tayyaba SK","unstructured":"Tayyaba SK, Shah MA, Khan OA, Ahmed AW, (2017, July). Software defined network (SDN) based Internet of Things (IoT): a road ahead. In: Proceedings of the international conference on future networks and distributed systems, 2017, p. Article 15. Cambridge, United Kingdom: Association for Computing Machinery.","journal-title":"Proceedings of the international conference on future networks and distributed systems"},{"key":"e_1_3_3_4_2","first-page":"7373","article-title":"Software-defined industrial Internet of Things in the context of Industry 4.0","volume":"16","author":"Wan J","year":"2016","unstructured":"Wan J, Tang S, Shu Z, Li D, Wang S, Imran M, Vasilakos AV. Software-defined industrial Internet of Things in the context of Industry 4.0. 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In: Proceedings of the 2018 10th international conference on machine learning and computing, pp.122\u2013127."},{"key":"e_1_3_3_10_2","first-page":"148","article-title":"Firefly algorithm based feature selection for network intrusion detection","volume":"81","author":"Selvakumar B","year":"2018","unstructured":"Selvakumar B, Muneeswaran K. Firefly algorithm based feature selection for network intrusion detection. Comput Secur 2018; 81: 148\u2013155.","journal-title":"Comput Secur"},{"key":"e_1_3_3_11_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.iot.2024.101231"},{"key":"e_1_3_3_12_2","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0290694"},{"key":"e_1_3_3_13_2","doi-asserted-by":"publisher","DOI":"10.1155\/2021\/5593214"},{"key":"e_1_3_3_14_2","first-page":"771","article-title":"DDoS detection in SDN using machine learning techniques","volume":"71","author":"Nadeem MW","year":"2021","unstructured":"Nadeem MW, Goh HG, Ponnusamy V, Aun Y. DDoS detection in SDN using machine learning techniques. Comput Mater Continua 2021; 71(1): 771\u2013789.","journal-title":"Comput Mater Continua"},{"key":"e_1_3_3_15_2","first-page":"1","article-title":"Explainable and data-efficient deep learning for enhanced attack detection in IIoT ecosystem","author":"Attique D","year":"2024","unstructured":"Attique D, Hao W, Ping W, Javeed D, Kumar P. Explainable and data-efficient deep learning for enhanced attack detection in IIoT ecosystem. IEEE Internet Things J 2024; PP: 1\u20131.","journal-title":"IEEE Internet Things J"},{"key":"e_1_3_3_16_2","doi-asserted-by":"publisher","DOI":"10.3390\/info14010041"},{"key":"e_1_3_3_17_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2022.3153716"},{"key":"e_1_3_3_18_2","doi-asserted-by":"crossref","unstructured":"Ferrag MA Friha O Hamouda D Maglaras L Janicke H. Edge-IIoTset: a new comprehensive realistic cyber security dataset of IoT and IIoT applications for centralized and federated learning. 2022; 10: 40281\u201340306.","DOI":"10.1109\/ACCESS.2022.3165809"},{"key":"e_1_3_3_19_2","doi-asserted-by":"publisher","DOI":"10.1049\/cit2.12352"},{"key":"e_1_3_3_20_2","first-page":"1","article-title":"X-IIoTID: a connectivity- and device-agnostic intrusion dataset for industrial internet of things","author":"Al-Hawawreh M","year":"2021","unstructured":"Al-Hawawreh M, Sitnikova E, Aboutorab N. X-IIoTID: a connectivity- and device-agnostic intrusion dataset for industrial internet of things. 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