{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,4]],"date-time":"2026-01-04T02:09:26Z","timestamp":1767492566851,"version":"3.48.0"},"reference-count":51,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2026,1,3]],"date-time":"2026-01-03T00:00:00Z","timestamp":1767398400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,3]],"date-time":"2026-01-03T00:00:00Z","timestamp":1767398400000},"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":["J Supercomput"],"DOI":"10.1007\/s11227-025-08118-5","type":"journal-article","created":{"date-parts":[[2026,1,4]],"date-time":"2026-01-04T02:07:57Z","timestamp":1767492477000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A novel metaheuristic-enhanced quantum-classical neural network for attack detection in agriculture IoT systems"],"prefix":"10.1007","volume":"82","author":[{"given":"Muhammed Furkan","family":"G\u00fcl","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Halit","family":"Bak\u0131r","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,1,3]]},"reference":[{"issue":"7671","key":"8118_CR1","doi-asserted-by":"publisher","first-page":"195","DOI":"10.1038\/nature23474","volume":"549","author":"J Biamonte","year":"2017","unstructured":"Biamonte J, Wittek P, Pancotti N, Rebentrost P, Wiebe N, Lloyd S (2017) Quantum machine learning. Nature 549(7671):195\u2013202","journal-title":"Nature"},{"issue":"1","key":"8118_CR2","doi-asserted-by":"publisher","DOI":"10.1002\/que2.34","volume":"2","author":"Y Zhang","year":"2020","unstructured":"Zhang Y, Ni Q (2020) Recent advances in quantum machine learning. Quantum Eng 2(1):e34","journal-title":"Quantum Eng"},{"key":"8118_CR3","doi-asserted-by":"publisher","first-page":"457","DOI":"10.1016\/j.neucom.2021.02.102","volume":"470","author":"JD Mart\u00edn-Guerrero","year":"2022","unstructured":"Mart\u00edn-Guerrero JD, Lamata L (2022) Quantum machine learning: A tutorial. Neurocomputing 470:457\u2013461","journal-title":"Neurocomputing"},{"issue":"1","key":"8118_CR4","doi-asserted-by":"publisher","first-page":"125","DOI":"10.1007\/s11416-022-00435-0","volume":"19","author":"M Kalinin","year":"2023","unstructured":"Kalinin M, Krundyshev V (2023) Security intrusion detection using quantum machine learning techniques. J Comput Virol Hacking Tech 19(1):125\u2013136","journal-title":"J Comput Virol Hacking Tech"},{"key":"8118_CR5","doi-asserted-by":"crossref","unstructured":"Shaikh TA, Ali R (2016) Quantum computing in big data analytics: A survey, In: IEEE International Conference on Computer and Information Technology (CIT). IEEE 2016:112\u2013115","DOI":"10.1109\/CIT.2016.79"},{"key":"8118_CR6","doi-asserted-by":"publisher","first-page":"114938","DOI":"10.1016\/j.tcs.2024.114938","volume":"1024","author":"B Singh","year":"2025","unstructured":"Singh B, Indu S, Majumdar S (2025) Comparison of machine learning algorithms for classification of big data sets. Theoret Comput Sci 1024:114938","journal-title":"Theoret Comput Sci"},{"issue":"01","key":"8118_CR7","doi-asserted-by":"publisher","first-page":"1","DOI":"10.4236\/jqis.2018.81001","volume":"8","author":"W Hu","year":"2018","unstructured":"Hu W et al (2018) Empirical analysis of a quantum classifier implemented on Ibm\u2019s 5q quantum computer. J Quantum Inf Sci 8(01):1","journal-title":"J Quantum Inf Sci"},{"key":"8118_CR8","doi-asserted-by":"crossref","unstructured":"Yang DL, Liu F, Liang Y-D (2010) A survey of the internet of things, In: 1st International Conference on E-Business Intelligence (ICEBI 2010), Atlantis Press, pp. 524\u2013532","DOI":"10.2991\/icebi.2010.72"},{"issue":"16","key":"8118_CR9","doi-asserted-by":"publisher","first-page":"7194","DOI":"10.3390\/s23167194","volume":"23","author":"R Chataut","year":"2023","unstructured":"Chataut R, Phoummalayvane A, Akl R (2023) Unleashing the power of iot: A comprehensive review of iot applications and future prospects in healthcare, agriculture, smart homes, smart cities, and industry 4.0. Sensors 23(16):7194","journal-title":"Sensors"},{"key":"8118_CR10","doi-asserted-by":"crossref","unstructured":"Rath KC, Khang A, Roy D (2024) The role of internet of things (iot) technology in industry 4.0 economy, In: Advanced IoT Technologies and Applications in the Industry 4.0 Digital Economy, CRC Press, pp. 1\u201328","DOI":"10.1201\/9781003434269-1"},{"issue":"9","key":"8118_CR11","doi-asserted-by":"publisher","first-page":"16","DOI":"10.1109\/MCOM.2017.1600514","volume":"55","author":"Y Mehmood","year":"2017","unstructured":"Mehmood Y, Ahmad F, Yaqoob I, Adnane A, Imran M, Guizani S (2017) Internet-of-things-based smart cities: Recent advances and challenges. IEEE Commun Mag 55(9):16\u201324","journal-title":"IEEE Commun Mag"},{"key":"8118_CR12","unstructured":"SonicWall, sonicwall.com, https:\/\/www.sonicwall.com\/resources\/white-papers\/2025-sonicwall-cyber-threat-report, [Accessed 11-04-2025]"},{"key":"8118_CR13","doi-asserted-by":"crossref","unstructured":"Adam M, Hammoudeh M, Alrawashdeh R, Alsulaimy B (2024) A survey on security, privacy, trust, and architectural challenges in iot systems, IEEE Access","DOI":"10.1109\/ACCESS.2024.3382709"},{"key":"8118_CR14","doi-asserted-by":"publisher","first-page":"31","DOI":"10.1016\/j.biosystemseng.2017.09.007","volume":"164","author":"A Tzounis","year":"2017","unstructured":"Tzounis A, Katsoulas N, Bartzanas T, Kittas C (2017) Internet of things in agriculture, recent advances and future challenges. Biosys Eng 164:31\u201348","journal-title":"Biosys Eng"},{"key":"8118_CR15","doi-asserted-by":"crossref","unstructured":"Verdouw C, Wolfert S, Tekinerdogan B (2016) Internet of things in agriculture., CABI Reviews 1\u201312","DOI":"10.1079\/PAVSNNR201611035"},{"key":"8118_CR16","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1016\/j.jpdc.2022.03.003","volume":"165","author":"O Friha","year":"2022","unstructured":"Friha O, Ferrag MA, Shu L, Maglaras L, Choo K-KR, Nafaa M (2022) Felids: Federated learning-based intrusion detection system for agricultural internet of things. J Parallel Distrib Comput 165:17\u201331","journal-title":"J Parallel Distrib Comput"},{"key":"8118_CR17","doi-asserted-by":"publisher","first-page":"109892","DOI":"10.1016\/j.compeleceng.2024.109892","volume":"121","author":"R Ferreira","year":"2025","unstructured":"Ferreira R, Bispo I, Rabad\u00e3o C, Santos L, Costa RLDC (2025) Farm-flow dataset: Intrusion detection in smart agriculture based on network flows. Comput Electr Eng 121:109892","journal-title":"Comput Electr Eng"},{"issue":"5","key":"8118_CR18","doi-asserted-by":"publisher","first-page":"3641","DOI":"10.1007\/s00521-024-10694-1","volume":"37","author":"C Zhong","year":"2025","unstructured":"Zhong C, Li G, Meng Z, Li H, Yildiz AR, Mirjalili S (2025) Starfish optimization algorithm (sfoa): a bio-inspired metaheuristic algorithm for global optimization compared with 100 optimizers. Neural Comput Appl 37(5):3641\u20133683","journal-title":"Neural Comput Appl"},{"key":"8118_CR19","doi-asserted-by":"crossref","unstructured":"G\u00fcl MF, Bakir H (2024) Improving attack detection in iov systems using ga-based hyperparameter optimization, In: 8th International Artificial Intelligence and Data Processing Symposium (IDAP). IEEE 2024:1\u20135","DOI":"10.1109\/IDAP64064.2024.10711086"},{"issue":"9","key":"8118_CR20","doi-asserted-by":"publisher","first-page":"13025","DOI":"10.1007\/s13369-024-08949-z","volume":"49","author":"H Bak\u0131r","year":"2024","unstructured":"Bak\u0131r H, Ceviz \u00d6 (2024) Empirical enhancement of intrusion detection systems: a comprehensive approach with genetic algorithm-based hyperparameter tuning and hybrid feature selection. Arab J Sci Eng 49(9):13025\u201313043","journal-title":"Arab J Sci Eng"},{"issue":"2","key":"8118_CR21","doi-asserted-by":"publisher","first-page":"1719","DOI":"10.1007\/s10586-023-04052-4","volume":"27","author":"K Kethineni","year":"2024","unstructured":"Kethineni K, Pradeepini G (2024) Intrusion detection in internet of things-based smart farming using hybrid deep learning framework. Clust Comput 27(2):1719\u20131732","journal-title":"Clust Comput"},{"issue":"1","key":"8118_CR22","doi-asserted-by":"publisher","first-page":"26","DOI":"10.1007\/s42484-024-00163-2","volume":"6","author":"M Hdaib","year":"2024","unstructured":"Hdaib M, Rajasegarar S, Pan L (2024) Quantum deep learning-based anomaly detection for enhanced network security. Quantum Mach Intell 6(1):26","journal-title":"Quantum Mach Intell"},{"issue":"1","key":"8118_CR23","doi-asserted-by":"publisher","first-page":"e497","DOI":"10.1002\/spy2.497","volume":"8","author":"R Ji","year":"2025","unstructured":"Ji R, Selwal A, Kumar N, Padha D (2025) Cascading bagging and boosting ensemble methods for intrusion detection in cyber-physical systems. Security Privacy 8(1):e497","journal-title":"Security Privacy"},{"issue":"9","key":"8118_CR24","doi-asserted-by":"publisher","first-page":"e5029","DOI":"10.1002\/ett.5029","volume":"35","author":"R Ji","year":"2024","unstructured":"Ji R, Padha D, Singh Y, Sharma S (2024) Review of intrusion detection system in cyber-physical system based networks: Characteristics, industrial protocols, attacks, data sets and challenges. Trans Emerg Telecommun Technol 35(9):e5029","journal-title":"Trans Emerg Telecommun Technol"},{"key":"8118_CR25","doi-asserted-by":"crossref","unstructured":"Ji R, Kumar N, Padha D (2024) Cnn-gwo-voting & hybrid: ensemble learning inspired intrusion detection approaches for cyber-physical systems, In: Proceedings of the Indian National Science Academy 1\u201315","DOI":"10.1007\/s43538-024-00372-0"},{"issue":"30","key":"8118_CR26","doi-asserted-by":"publisher","first-page":"3069","DOI":"10.17485\/IJST\/v17i30.1794","volume":"17","author":"R Ji","year":"2024","unstructured":"Ji R, Kumar N, Padha D (2024) Hybrid enhanced intrusion detection frameworks for cyber-physical systems via optimal features selection. Indian J Sci Technol 17(30):3069\u20133079","journal-title":"Indian J Sci Technol"},{"issue":"3","key":"8118_CR27","doi-asserted-by":"publisher","first-page":"e70031","DOI":"10.1002\/spy2.70031","volume":"8","author":"R Ji","year":"2025","unstructured":"Ji R, Kumar N, Padha D (2025) Optimized intrusion detection approach for cyber-physical system using meta-learning with stacked generalization: An ensemble learning inspired approach. Security Privacy 8(3):e70031","journal-title":"Security Privacy"},{"issue":"1","key":"8118_CR28","first-page":"3955514","volume":"2022","author":"A Raghuvanshi","year":"2022","unstructured":"Raghuvanshi A, Singh UK, Sajja GS, Pallathadka H, Asenso E, Kamal M, Singh A, Phasinam K (2022) Intrusion detection using machine learning for risk mitigation in iot-enabled smart irrigation in smart farming. J Food Qual 2022(1):3955514","journal-title":"J Food Qual"},{"key":"8118_CR29","doi-asserted-by":"publisher","first-page":"108579","DOI":"10.1016\/j.engappai.2024.108579","volume":"133","author":"K Zidi","year":"2024","unstructured":"Zidi K, Abdellafou KB, Aljuhani A, Taouali O, Harkat MF (2024) Novel intrusion detection system based on a downsized kernel method for cybersecurity in smart agriculture. Eng Appl Artif Intell 133:108579","journal-title":"Eng Appl Artif Intell"},{"key":"8118_CR30","doi-asserted-by":"publisher","first-page":"16621","DOI":"10.1109\/ACCESS.2024.3359043","volume":"12","author":"RY Aburasain","year":"2024","unstructured":"Aburasain RY (2024) Enhanced black widow optimization with hybrid deep learning enabled intrusion detection in internet of things-based smart farming. IEEE Access 12:16621\u201316631","journal-title":"IEEE Access"},{"key":"8118_CR31","doi-asserted-by":"publisher","first-page":"46601","DOI":"10.1109\/ACCESS.2025.3550800","volume":"13","author":"H Zhou","year":"2025","unstructured":"Zhou H, Zou H, Zhou P, Shen Y, Li D, Li W (2025) Cbctl-ids: A transfer learning-based intrusion detection system optimized with the black kite algorithm for iot-enabled smart agriculture. IEEE Access 13:46601\u201346615","journal-title":"IEEE Access"},{"key":"8118_CR32","doi-asserted-by":"publisher","first-page":"101422","DOI":"10.1016\/j.iot.2024.101422","volume":"29","author":"R Saadouni","year":"2025","unstructured":"Saadouni R, Gherbi C, Aliouat Z, Harbi Y, Khacha A, Mabed H (2025) Securing smart agriculture networks using bio-inspired feature selection and transfer learning for effective image-based intrusion detection. Int Things 29:101422","journal-title":"Int Things"},{"key":"8118_CR33","first-page":"100476","volume":"26","author":"S Li","year":"2024","unstructured":"Li S, Wang Z, Yang S, Luo X, He D, Chan S (2024) Internet of things intrusion detection: Research and practice of nsenet and lstm fusion models. Egypt Inf J 26:100476","journal-title":"Egypt Inf J"},{"key":"8118_CR34","doi-asserted-by":"publisher","first-page":"124965","DOI":"10.1016\/j.eswa.2024.124965","volume":"257","author":"Z Li","year":"2024","unstructured":"Li Z, Yao W (2024) A two stage lightweight approach for intrusion detection in internet of things. Expert Syst Appl 257:124965","journal-title":"Expert Syst Appl"},{"issue":"1","key":"8118_CR35","doi-asserted-by":"publisher","first-page":"285","DOI":"10.1007\/s11276-023-03435-0","volume":"30","author":"A Kaushik","year":"2024","unstructured":"Kaushik A, Al-Raweshidy H (2024) A novel intrusion detection system for internet of things devices and data. Wireless Netw 30(1):285\u2013294","journal-title":"Wireless Netw"},{"issue":"4","key":"8118_CR36","doi-asserted-by":"publisher","first-page":"2173","DOI":"10.1007\/s11276-023-03637-6","volume":"30","author":"H Ghasemi","year":"2024","unstructured":"Ghasemi H, Babaie S (2024) A new intrusion detection system based on svm-gwo algorithms for internet of things. Wireless Netw 30(4):2173\u20132185","journal-title":"Wireless Netw"},{"key":"8118_CR37","doi-asserted-by":"publisher","first-page":"104072","DOI":"10.1016\/j.jnca.2024.104072","volume":"234","author":"R Kumar","year":"2025","unstructured":"Kumar R, Swarnkar M (2025) Quids: A quantum support vector machine-based intrusion detection system for iot networks. J Netw Comput Appl 234:104072","journal-title":"J Netw Comput Appl"},{"key":"8118_CR38","doi-asserted-by":"crossref","unstructured":"Bellante A, Fioravanti T, Carminati M, Zanero S, Luongo A (2025) Evaluating the potential of quantum machine learning in cybersecurity: A case-study on pca-based intrusion detection systems, Comput Security 104341","DOI":"10.1016\/j.cose.2025.104341"},{"key":"8118_CR39","doi-asserted-by":"crossref","unstructured":"Al-Hawawreh M, Hossain MS (2025) A human-centered quantum machine learning framework for attack detection in iot-based healthcare industry 5.0, IEEE Int Things J","DOI":"10.1109\/JIOT.2025.3565687"},{"issue":"7","key":"8118_CR40","doi-asserted-by":"publisher","first-page":"9917","DOI":"10.1007\/s10586-024-04458-8","volume":"27","author":"E Elsedimy","year":"2024","unstructured":"Elsedimy E, Elhadidy H, Abohashish SM (2024) A novel intrusion detection system based on a hybrid quantum support vector machine and improved grey wolf optimizer. Clust Comput 27(7):9917\u20139935","journal-title":"Clust Comput"},{"issue":"8","key":"8118_CR41","doi-asserted-by":"publisher","first-page":"3572","DOI":"10.3390\/en16083572","volume":"16","author":"D Said","year":"2023","unstructured":"Said D (2023) Quantum computing and machine learning for cybersecurity: Distributed denial of service (ddos) attack detection on smart micro-grid. Energies 16(8):3572","journal-title":"Energies"},{"key":"8118_CR42","doi-asserted-by":"publisher","first-page":"39","DOI":"10.1016\/j.aej.2024.01.073","volume":"91","author":"NO Aljehane","year":"2024","unstructured":"Aljehane NO, Mengash HA, Hassine SB, Alotaibi FA, Salama AS, Abdelbagi S (2024) Optimizing intrusion detection using intelligent feature selection with machine learning model. Alex Eng J 91:39\u201349","journal-title":"Alex Eng J"},{"issue":"8","key":"8118_CR43","doi-asserted-by":"publisher","first-page":"271","DOI":"10.3390\/fi15080271","volume":"15","author":"AS Rajawat","year":"2023","unstructured":"Rajawat AS, Goyal S, Bedi P, Jan T, Whaiduzzaman M, Prasad M (2023) Quantum machine learning for security assessment in the internet of medical things (iomt). Future Int 15(8):271","journal-title":"Future Int"},{"issue":"2","key":"8118_CR44","doi-asserted-by":"publisher","first-page":"287","DOI":"10.3390\/e25020287","volume":"25","author":"A Zeguendry","year":"2023","unstructured":"Zeguendry A, Jarir Z, Quafafou M (2023) Quantum machine learning: A review and case studies. Entropy 25(2):287","journal-title":"Entropy"},{"issue":"11","key":"8118_CR45","doi-asserted-by":"publisher","first-page":"2379","DOI":"10.3390\/electronics12112379","volume":"12","author":"KA Tychola","year":"2023","unstructured":"Tychola KA, Kalampokas T, Papakostas GA (2023) Quantum machine learning-an overview. Electronics 12(11):2379","journal-title":"Electronics"},{"key":"8118_CR46","doi-asserted-by":"crossref","unstructured":"Riel H (2021) Quantum computing technology, In: IEEE International Electron Devices Meeting (IEDM). IEEE 2021:1\u20133","DOI":"10.1109\/IEDM19574.2021.9720538"},{"key":"8118_CR47","doi-asserted-by":"crossref","unstructured":"Williams CP, Williams CP (2011) Quantum gates, Explorations in quantum computing 51\u2013122","DOI":"10.1007\/978-1-84628-887-6_2"},{"key":"8118_CR48","doi-asserted-by":"publisher","first-page":"126843","DOI":"10.1016\/j.neucom.2023.126843","volume":"560","author":"R Kharsa","year":"2023","unstructured":"Kharsa R, Bouridane A, Amira A (2023) Advances in quantum machine learning and deep learning for image classification: A survey. Neurocomputing 560:126843","journal-title":"Neurocomputing"},{"issue":"3","key":"8118_CR49","doi-asserted-by":"publisher","first-page":"032420","DOI":"10.1103\/PhysRevA.102.032420","volume":"102","author":"R LaRose","year":"2020","unstructured":"LaRose R, Coyle B (2020) Robust data encodings for quantum classifiers. Phys Rev A 102(3):032420","journal-title":"Phys Rev A"},{"issue":"4","key":"8118_CR50","doi-asserted-by":"publisher","first-page":"043001","DOI":"10.1088\/2058-9565\/ab4eb5","volume":"4","author":"M Benedetti","year":"2019","unstructured":"Benedetti M, Lloyd E, Sack S, Fiorentini M (2019) Parameterized quantum circuits as machine learning models. Quantum Sci Technol 4(4):043001","journal-title":"Quantum Sci Technol"},{"issue":"9","key":"8118_CR51","doi-asserted-by":"publisher","first-page":"625","DOI":"10.1038\/s42254-021-00348-9","volume":"3","author":"M Cerezo","year":"2021","unstructured":"Cerezo M, Arrasmith A, Babbush R, Benjamin SC, Endo S, Fujii K, McClean JR, Mitarai K, Yuan X, Cincio L et al (2021) Variational quantum algorithms. Nature Rev Phys 3(9):625\u2013644","journal-title":"Nature Rev Phys"}],"container-title":["The Journal of Supercomputing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-025-08118-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11227-025-08118-5","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-025-08118-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,4]],"date-time":"2026-01-04T02:07:59Z","timestamp":1767492479000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11227-025-08118-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,1,3]]},"references-count":51,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2026,1]]}},"alternative-id":["8118"],"URL":"https:\/\/doi.org\/10.1007\/s11227-025-08118-5","relation":{},"ISSN":["1573-0484"],"issn-type":[{"value":"1573-0484","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,1,3]]},"assertion":[{"value":"27 July 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 November 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 January 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":"The authors declare that they have no known competing financial interests or personal relationships that may have influenced the work in this article.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"This article does not contain any studies with human participants or animals performed by any of the authors.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}}],"article-number":"44"}}