{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,16]],"date-time":"2026-04-16T10:52:09Z","timestamp":1776336729826,"version":"3.51.2"},"reference-count":53,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T00:00:00Z","timestamp":1776038400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2026,4,16]],"date-time":"2026-04-16T00:00:00Z","timestamp":1776297600000},"content-version":"vor","delay-in-days":3,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Cloud Comp"],"DOI":"10.1186\/s13677-026-00897-3","type":"journal-article","created":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T13:53:55Z","timestamp":1776088435000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Feature resonance classifier: a novel neural network for efficient network traffic classification"],"prefix":"10.1186","volume":"15","author":[{"given":"Mahmoud E.","family":"Farfoura","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Radwan M.","family":"Batyha","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ahmed Zohair","family":"Ibrahim","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Faten Khalid","family":"Karim","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,4,13]]},"reference":[{"key":"897_CR1","unstructured":"Ericsson (2024) Ericsson mobility report, November 2024. [Online]. Available: https:\/\/www.ericsson.com\/en\/reports-and-papers\/mobility-report\/documents\/2024\/ericsson-mobility-report-november-2024.pdf"},{"issue":"12","key":"897_CR2","doi-asserted-by":"publisher","first-page":"1549","DOI":"10.3844\/jcssp.2023.1549.1560","volume":"19","author":"HM Al-Mimi","year":"2023","unstructured":"Al-Mimi HM, Hamad NA, Abualhaj MM, Al-Khatib SN, Hiari MO (2023) Improved intrusion detection system to alleviate attacks on DNS service. J Comput Sci 19(12):1549\u20131560","journal-title":"J Comput Sci"},{"issue":"6","key":"897_CR3","doi-asserted-by":"publisher","first-page":"3819","DOI":"10.1007\/s10586-022-03604-4","volume":"25","author":"A Mughaid","year":"2022","unstructured":"Mughaid A, AlZu\u2019bi S, Hnaif A, Taamneh S, Alnajjar A (2022 Dec) An intelligent cyber security phishing detection system using deep learning techniques. Cluster Comput 25(6):3819\u20133828. https:\/\/doi.org\/10.1007\/s10586-022-03604-4","journal-title":"Cluster Comput"},{"key":"897_CR4","doi-asserted-by":"publisher","unstructured":"Farfoura ME, Mashal I, Alkhatib A, Batyha RM, Rosiyadi D (2025 Jan) A novel lightweight machine learning framework for IoT malware classification based on matrix block mean downsampling. Ain Shams Eng J 16(1), Art. no. 103205. https:\/\/doi.org\/10.1016\/j.asej.2024.103205","DOI":"10.1016\/j.asej.2024.103205"},{"key":"897_CR5","unstructured":"Verizon (2024) 2024 data breach investigations report (DBIR). [Online]. Available: https:\/\/www.verizon.com\/business\/resources\/reports\/2024-dbir-data-breach-investigations-report.pdf"},{"key":"897_CR6","unstructured":"CISA (2024) Known exploited vulnerabilities catalog. [Online]. Available: https:\/\/www.cisa.gov\/known-exploited-vulnerabilities-catalog"},{"key":"897_CR7","doi-asserted-by":"publisher","unstructured":"Neto ECP, Dadkhah S, Ferreira R, Zohourian A, Lu R, Ghorbani AA (2023 Jun) CICIoT2023: a real-time dataset and benchmark for large-scale attacks in IoT environment. Sensors 23(13), Art. no. 5941. https:\/\/doi.org\/10.3390\/s23135941","DOI":"10.3390\/s23135941"},{"key":"897_CR8","doi-asserted-by":"publisher","unstructured":"Aldweesh A, Derhab A, Emam AZ (2020 Feb) Deep learning approaches for anomaly-based intrusion detection systems: a survey, taxonomy, and open issues. Knowl-Based Syst 189, Art. no. 105124. https:\/\/doi.org\/10.1016\/j.knosys.2019.105124","DOI":"10.1016\/j.knosys.2019.105124"},{"key":"897_CR9","doi-asserted-by":"publisher","unstructured":"Ta\u015fc\u0131 B (2024 Sep) Deep-learning-based approach for IoT attack and malware detection. Appl Sci 14(18), Art. no. 8505. https:\/\/doi.org\/10.3390\/app14188505","DOI":"10.3390\/app14188505"},{"key":"897_CR10","doi-asserted-by":"publisher","unstructured":"Kantharaju V, Suresh H, Niranjanamurthy M, Ansarullah SI, Amin F, Alabrah A (2024 Dec) Machine learning based intrusion detection framework for detecting security attacks in internet of things. Sci Rep 14(1), Art. no. 30275. https:\/\/doi.org\/10.1038\/s41598-024-81535-3","DOI":"10.1038\/s41598-024-81535-3"},{"key":"897_CR11","doi-asserted-by":"publisher","unstructured":"Xi C, Wang H, Wang X (2024 Oct) A novel multi-scale network intrusion detection model with transformer. Sci Rep 14, Art. no. 23239. https:\/\/doi.org\/10.1038\/s41598-024-74214-w","DOI":"10.1038\/s41598-024-74214-w"},{"key":"897_CR12","doi-asserted-by":"crossref","unstructured":"Wang K, Zhang H, Liu L, Chen J (2024 Dec) ImagTIDS: an internet of things intrusion detection framework utilizing GADF imaging encoding and improved Transformer. Complex Intell Syst, 1\u201324.","DOI":"10.1007\/s40747-024-01712-9"},{"key":"897_CR13","doi-asserted-by":"crossref","unstructured":"Zhang Y, Fan Y (2024) An efficient CNN + sparse Transformer-based intrusion detection method for IoT. In: Advanced intelligent computing technology and applications. Springer, Singapore, pp 443\u2013454","DOI":"10.1007\/978-981-97-5609-4_38"},{"key":"897_CR14","doi-asserted-by":"publisher","unstructured":"Almotairi A, Atawneh S, Khashan OA, Khafajah NM (2024) Enhancing intrusion detection in IoT networks using machine learning-based feature selection and ensemble models. Syst Sci Control Eng 12(1), Art. no. 2321381. https:\/\/doi.org\/10.1080\/21642583.2024.2321381","DOI":"10.1080\/21642583.2024.2321381"},{"key":"897_CR15","doi-asserted-by":"publisher","unstructured":"Benaddi H, Jouhari M, Ibrahimi K, Benslimane A, Amhoud EM (2024 May) Ensemble technique of intrusion detection for IoT-edge platform. Sci Rep 14, Art. no. 11723. https:\/\/doi.org\/10.1038\/s41598-024-624xx-x.","DOI":"10.1038\/s41598-024-624xx-x"},{"key":"897_CR16","doi-asserted-by":"publisher","unstructured":"Haque S, El-Moussa F, Komninos N, Muttukrishnan R (2023 Aug) A systematic review of data-driven attack detection trends in IoT. Sensors 23(16), Art. no. 7191. https:\/\/doi.org\/10.3390\/s23167191","DOI":"10.3390\/s23167191"},{"key":"897_CR17","doi-asserted-by":"publisher","unstructured":"Hnamte V, Nhung-Nguyen H, Hussain J, Hwa-Kim Y (2024 Nov) An automated intrusion detection system in IoT system using attention-based deep bidirectional sparse autoencoder model. Knowl-Based Syst 305, Art. no. 112633. https:\/\/doi.org\/10.1016\/j.knosys.2024.112633","DOI":"10.1016\/j.knosys.2024.112633"},{"issue":"1","key":"897_CR18","first-page":"823","volume":"141","author":"MA Alsoufi","year":"2024","unstructured":"Alsoufi MA et al (2024 Aug) Anomaly-based intrusion detection model using deep learning for IoT networks. Comput Model Eng Sci 141(1):823\u2013845","journal-title":"Comput Model Eng Sci"},{"issue":"3","key":"897_CR19","doi-asserted-by":"publisher","first-page":"541","DOI":"10.1007\/s10207-022-00602-w","volume":"22","author":"A Huertas Celdr\u00e1n","year":"2023","unstructured":"Huertas Celdr\u00e1n A, S\u00e1nchez S\u00e1nchez PM, Azor\u00edn Castillo M, Bovet G, Mart\u00ednez P\u00e9rez G, Stiller B (2023 Jul) Intelligent and behavioral-based detection of malware in IoT spectrum sensors. Int J Inf Secur 22(3):541\u2013561. https:\/\/doi.org\/10.1007\/s10207-022-00602-w","journal-title":"Int J Inf Secur"},{"issue":"3","key":"897_CR20","doi-asserted-by":"publisher","first-page":"509","DOI":"10.3390\/ai4030028","volume":"4","author":"R Lazzarini","year":"2023","unstructured":"Lazzarini R, Tianfield H, Charissis V (2023 Jul) Federated learning for IoT intrusion detection. AI 4(3):509\u2013530. https:\/\/doi.org\/10.3390\/ai4030028","journal-title":"AI"},{"key":"897_CR21","doi-asserted-by":"crossref","unstructured":"Olanrewaju-George B, Pranggono B (2025) Federated learning-based intrusion detection system for the internet of things using unsupervised and supervised deep learning models. Cyber Secur Appl 3, Art. no. 100068","DOI":"10.1016\/j.csa.2024.100068"},{"key":"897_CR22","doi-asserted-by":"crossref","unstructured":"Yadav SK et al (2025 Mar) An optimal federated learning-based intrusion detection for IoT environment. Sci Rep 15, Art. no. 93501","DOI":"10.1038\/s41598-025-93501-8"},{"key":"897_CR23","doi-asserted-by":"crossref","unstructured":"Ahmad A, Haroon M, Shah A, Khan A (2024 Oct) Privacy-preserving federated learning-based intrusion detection technique for cyber-physical systems. Mathematics 12(20), Art. no. 3194","DOI":"10.3390\/math12203194"},{"key":"897_CR24","doi-asserted-by":"publisher","unstructured":"Nguyen VT, Beuran R (2025) FedMSE: semi-supervised federated learning approach for IoT network intrusion detection. Comput Secur 151, Art. no. 104337. https:\/\/doi.org\/10.1016\/j.cose.2025.104337","DOI":"10.1016\/j.cose.2025.104337"},{"key":"897_CR25","doi-asserted-by":"publisher","unstructured":"Amiri-Zarandi M, Dara RA, Lin X (2023 Nov) SIDS: a federated learning approach for intrusion detection in IoT using social Internet of Things. Comput Netw 236, Art. no. 110005. https:\/\/doi.org\/10.1016\/j.comnet.2023.110005","DOI":"10.1016\/j.comnet.2023.110005"},{"key":"897_CR26","doi-asserted-by":"publisher","unstructured":"Visoottiviseth V, Sakarin P, Thongwilai J, Choobanjong T (2020 Nov) Signature-based and behavior-based attack detection with machine learning for home IoT devices. In: Proc. IEEE Region 10 Conf. (TENCON), Osaka, Japan, pp 829\u2013834. https:\/\/doi.org\/10.1109\/TENCON50793.2020.9293811","DOI":"10.1109\/TENCON50793.2020.9293811"},{"key":"897_CR27","doi-asserted-by":"publisher","unstructured":"Ali S, Li Q, Yousafzai A (2023 Oct) Blockchain and federated learning-based intrusion detection approaches for edge-enabled industrial IoT networks: a survey. Ad Hoc Netw 152, Art. no. 103320. https:\/\/doi.org\/10.1016\/j.adhoc.2023.103320","DOI":"10.1016\/j.adhoc.2023.103320"},{"key":"897_CR28","doi-asserted-by":"publisher","unstructured":"Zhukabayeva T, Ahmad Z, Adamova A, Karabayev N, Abdildayeva A (2025 Apr) An edge-computing-based integrated framework for network traffic analysis and intrusion detection to enhance cyber\u2013physical system security in industrial IoT. Sensors 25(8), Art. no. 2395. https:\/\/doi.org\/10.3390\/s25082395","DOI":"10.3390\/s25082395"},{"key":"897_CR29","doi-asserted-by":"crossref","unstructured":"Alwaisi Z, Kumar T, Harjula E, Ylianttila M, Sepp\u00e4nen T (2024 Oct) Securing constrained IoT systems: a lightweight machine learning approach for anomaly detection and prevention. Internet Things 28, Art. no. 101339.","DOI":"10.1016\/j.iot.2024.101398"},{"key":"897_CR30","doi-asserted-by":"publisher","unstructured":"Wang Z, Zhang Y, Wang L, Green M (2024 Mar) An efficient intrusion detection model based on convolutional spiking neural network. Sci Rep 14, Art. no. 7054. https:\/\/doi.org\/10.1038\/s41598-024-57691-x.\u00a0","DOI":"10.1038\/s41598-024-57691-x"},{"key":"897_CR31","unstructured":"Microsoft (2024) Cyber signals report. [Online]. Available: https:\/\/www.microsoft.com\/en-us\/security\/blog\/tag\/cyber-signals\/"},{"key":"897_CR32","doi-asserted-by":"publisher","DOI":"10.1093\/wentk\/9780199918096.001.0001","volume-title":"Cybersecurity and cyberwar: what everyone needs to Know\u00ae","author":"PW Singer","year":"2014","unstructured":"Singer PW, Friedman A (2014) Cybersecurity and cyberwar: what everyone needs to Know\u00ae. Oxford Univ. Press, New York, NY, USA"},{"key":"897_CR33","unstructured":"Wazuh Inc (2024) Wazuh - open source XDR. Open source SIEM. [Online]. Available: https:\/\/wazuh.com\/"},{"key":"897_CR34","unstructured":"Roesch M (1999 Nov) Snort: lightweight intrusion detection for networks. In: Proc. 13th USENIX Conf. Syst. Admin. (LISA), Seattle, WA, USA, pp 229\u2013238"},{"key":"897_CR35","doi-asserted-by":"crossref","unstructured":"Ferrag MA, Friha O, Hamouda D, Maglaras L, Janicke H (2022). Edge-IIoTset: a new comprehensive realistic cyber security dataset of IoT and IIoT applications for centralized and federated learning. IEEE DataPort. [Dataset]. [Online]. Available: https:\/\/ieee-dataport.org\/documents\/edge-iiotset-new-comprehensive-realistic-cyber-security-dataset-iot-and-iiot-applications","DOI":"10.36227\/techrxiv.18857336.v1"},{"key":"897_CR36","doi-asserted-by":"publisher","unstructured":"Sharma A, Bhushan K (2026 Feb) A comprehensive survey on IoT security: challenges, security issues, and countermeasures. Comput Sci Rev 59, Art. no. 100839. https:\/\/doi.org\/10.1016\/j.cosrev.2025.100839.","DOI":"10.1016\/j.cosrev.2025.100839"},{"key":"897_CR37","doi-asserted-by":"publisher","unstructured":"Rahman MM, Shakil SA, Mustakim MR (2025 Dec) A survey on intrusion detection system in IoT networks. Cyber Secur Appl 3, Art. no. 100082. https:\/\/doi.org\/10.1016\/j.csa.2024.100082.\u00a0","DOI":"10.1016\/j.csa.2024.100082"},{"key":"897_CR38","doi-asserted-by":"publisher","unstructured":"Alotaibi B et al (2023) A survey on industrial internet of things security: requirements, attacks, AI-based solutions, and edge computing opportunities. Sensors 23(17), Art. no. 7470. https:\/\/doi.org\/10.3390\/s23177470.\u00a0","DOI":"10.3390\/s23177470"},{"key":"897_CR39","doi-asserted-by":"publisher","unstructured":"Khraisat A, Alazab A (2021 Dec) A critical review of intrusion detection systems in the Internet of Things: techniques, deployment strategy, validation strategy, attacks, public datasets and challenges. Cybersecurity 4(1), Art. no. 18. https:\/\/doi.org\/10.1186\/s42400-021-00077-7.","DOI":"10.1186\/s42400-021-00077-7"},{"key":"897_CR40","doi-asserted-by":"publisher","unstructured":"Zhang C, Li J, Wang N, Zhang D (2025 Apr) Research on intrusion detection method based on Transformer and CNN-BiLSTM in Internet of Things. Sensors 25(9), Art. no. 2725. https:\/\/doi.org\/10.3390\/s25092725","DOI":"10.3390\/s25092725"},{"key":"897_CR41","doi-asserted-by":"publisher","unstructured":"Yang K et al. (2024 Aug) An improved intrusion detection method for IIoT using attention mechanisms, BiGRU, and inception-CNN. Sci Rep 14, Art. no. 19339. https:\/\/doi.org\/10.1038\/s41598-024-70094-2.\u00a0","DOI":"10.1038\/s41598-024-70094-2"},{"key":"897_CR42","doi-asserted-by":"publisher","unstructured":"Alahmadi AA et al. (2023 Jul) DDoS attack detection in IoT-based networks using machine learning models: a survey and research directions. Electronics 12(14), Art. no. 3103. https:\/\/doi.org\/10.3390\/electronics12143103","DOI":"10.3390\/electronics12143103"},{"key":"897_CR43","doi-asserted-by":"publisher","first-page":"146","DOI":"10.1016\/j.comnet.2014.11.008","volume":"76","author":"S Sicari","year":"2015","unstructured":"Sicari S, Rizzardi A, Grieco LA, Coen-Porisini A (2015 Jan) Security, privacy and trust in internet of things: the road ahead. Comput Netw 76:146\u2013164. https:\/\/doi.org\/10.1016\/j.comnet.2014.11.008","journal-title":"Comput Netw"},{"key":"897_CR44","doi-asserted-by":"publisher","unstructured":"Profentzas C, Almgren M, Landsiedel O (2021 May) Performance of deep neural networks on low-power IoT devices. In: Proc. Benchmarking Cyber-Phys. Syst. Internet Things (CPS-IoTBench), Nashville, TN, USA, pp 1\u20136. https:\/\/doi.org\/10.1145\/3458473.3458823","DOI":"10.1145\/3458473.3458823"},{"key":"897_CR45","doi-asserted-by":"publisher","first-page":"165130","DOI":"10.1109\/ACCESS.2020.3022862","volume":"8","author":"A Alsaedi","year":"2020","unstructured":"Alsaedi A, Moustafa N, Tari Z, Mahmood A, Anwar A (2020) Ton_iot telemetry dataset: a new generation dataset of IoT and IIoT for data-driven intrusion detection systems. IEEE Access. 8:165130\u2013165150. https:\/\/doi.org\/10.1109\/ACCESS.2020.3022862","journal-title":"IEEE Access."},{"key":"897_CR46","unstructured":"Vaswani A et al (2017) Attention is all you need. In: Proc. Adv. Neural Inf. Process. Syst. (NeurIPS), pp 5998\u20136008"},{"key":"897_CR47","unstructured":"Kingma DP, Ba J (2014) Adam: a method for stochastic optimization,\u201d arXiv preprint arXiv:1412.6980"},{"key":"897_CR48","unstructured":"Paszke A et al (2019) PyTorch: an imperative style, high-performance deep learning library. In: Proc. Adv. Neural Inf. Process. Syst. (NeurIPS), pp 8024\u20138035"},{"key":"897_CR49","unstructured":"psutil documentation (2024). [Online]. Available: https:\/\/psutil.readthedocs.io\/"},{"key":"897_CR50","unstructured":"Thop (pytorch-OpCounter) documentation (2024). [Online]. Available: https:\/\/github.com\/Lyken17\/pytorch-OpCounter"},{"key":"897_CR51","doi-asserted-by":"publisher","unstructured":"Sharmila BS, Nagapadma R (2023) RT-IoT2022. UCI Mach Learn Repository. [Online]. Available: https:\/\/doi.org\/10.24432\/C5P338","DOI":"10.24432\/C5P338"},{"key":"897_CR52","doi-asserted-by":"publisher","unstructured":"Merenda M, Porcaro C, Iero D (2020 May) Edge machine learning for AI-enabled IoT devices: a review. Sensors 20(9), Art. no. 2533. https:\/\/doi.org\/10.3390\/s20092533","DOI":"10.3390\/s20092533"},{"issue":"7","key":"897_CR53","doi-asserted-by":"publisher","first-page":"5113","DOI":"10.1007\/s10462-020-9816-7","volume":"53","author":"T Choudhary","year":"2020","unstructured":"Choudhary T, Mishra V, Goswami A, Sarangapani J (2020 Oct) A comprehensive survey on model compression and acceleration. Artif Intell Rev 53(7):5113\u20135155. https:\/\/doi.org\/10.1007\/s10462-020-9816-7","journal-title":"Artif Intell Rev"}],"container-title":["Journal of Cloud Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s13677-026-00897-3","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13677-026-00897-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13677-026-00897-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,16]],"date-time":"2026-04-16T10:01:34Z","timestamp":1776333694000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1186\/s13677-026-00897-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4,13]]},"references-count":53,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["897"],"URL":"https:\/\/doi.org\/10.1186\/s13677-026-00897-3","relation":{},"ISSN":["2192-113X"],"issn-type":[{"value":"2192-113X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,4,13]]},"assertion":[{"value":"5 September 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 March 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 April 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"This article does not contain any studies involving human participants or animals performed by any of the authors. The research is based entirely on publicly available network traffic datasets (CICIoT2023 and RT-IoT2022), which are anonymized and do not include any personally identifiable information. Therefore, ethical approval and informed consent were not required for this study. The work complies with standard ethical guidelines for research using publicly available data and adheres to principles of responsible data usage and reproducibility.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval and informed consent"}},{"value":"All authors have given their informed permission to publish this research study. Ethical guidelines guided the investigation, and every participant was completely informed on the nature and goal of the research. Before each participant was included in the study, written permission was obtained from each one. To further respect participants\u2019 privacy, all identifiable data has also been anonymized. This guarantees that the release of this study follows moral standards and protects the rights of the affected people.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"61"}}