{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T03:58:27Z","timestamp":1782964707762,"version":"3.54.5"},"reference-count":51,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,1,29]],"date-time":"2026-01-29T00:00:00Z","timestamp":1769644800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,29]],"date-time":"2026-01-29T00:00:00Z","timestamp":1769644800000},"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":["Memetic Comp."],"published-print":{"date-parts":[[2026,3]]},"DOI":"10.1007\/s12293-025-00490-2","type":"journal-article","created":{"date-parts":[[2026,1,29]],"date-time":"2026-01-29T09:08:03Z","timestamp":1769677683000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["ML-BoTDAM: machine learning driven botnet detection and alerting mechanism"],"prefix":"10.1007","volume":"18","author":[{"family":"Happy","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rita","family":"Chhikara","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Neeti","family":"Kashyap","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,1,29]]},"reference":[{"key":"490_CR1","unstructured":"Transforma Iinsights. Global IoT forecast report, 2023\u20132033. [Online] Available: https:\/\/transformainsights.com\/research\/reports\/global-iot-forecast-report-2023-2033. Acessed May 2024"},{"key":"490_CR2","unstructured":"Statista. Internet of Things (IoT) total annual revenue worldwide from 2020 to 2030. [Online] Available: https:\/\/www.statista.com\/statistics\/1194709\/iot-revenue-worldwide\/. Accessed May 2024"},{"key":"490_CR3","unstructured":"IoT Analytics. State of IoT 2023: number of connected IoT devices growing 16% to 16.7 billion globally. [Online] Available: https:\/\/iot-analytics.com\/number-connected-iot-devices\/. Accessed May 2024"},{"key":"490_CR4","unstructured":"Check Point Research (CPR). 2024 cyber security report [Online] Available: https:\/\/go.checkpoint.com\/2024-cyber-security-report\/. Accessed May 2024"},{"key":"490_CR5","unstructured":"Asimily. IoT device security in 2024: the high cost of doing nothing. [Online] Available: https:\/\/6743964.fs1.hubspotusercontent-na1.net\/hubfs\/6743964\/IoT%20Device%20Security%20in%202024%2c%20Asimily.pdf. Accessed May 2024"},{"key":"490_CR6","unstructured":"Zscaler. Zscaler ThreatLabz 2023 enterprise IoT and OT threat report. [Online] Available: https:\/\/www.zscaler.com\/resources\/industry-reports\/threatlabz-2023-enterprise-ioT-ot-threat-report.pdf. Accessed May 2024"},{"key":"490_CR7","unstructured":"The Spamhaus Team. Malware digest January 2024. [Online] Available: https:\/\/www.spamhaus.org\/resource-hub\/malware\/malware-digest-january-2024\/. Accessed May 2024"},{"key":"490_CR8","unstructured":"The Spamhaus Team. Botnet threat update Q4 2023. [Online] Available: https:\/\/www.spamhaus.org\/resource-hub\/botnet-c-c\/botnet-threat-update-q4-2023\/. Accessed May 2024"},{"key":"490_CR9","unstructured":"Kespersky. Kespersky security bulletin 2023. [Online] Available: https:\/\/media.kasperskycontenthub.com\/wp-content\/uploads\/sites\/43\/2023\/11\/28102415\/KSB_statistics_2023_en.pdf. Accessed May 2024"},{"issue":"13","key":"490_CR10","doi-asserted-by":"publisher","DOI":"10.3390\/s23135941","volume":"23","author":"ECP Neto","year":"2023","unstructured":"Neto ECP, Dadkhah S, Ferreira R, Zohourian A, Lu R, Ghorbani AA (2023) CICIoT2023: a real-time dataset and benchmark for large-scale attacks in IoT environment. Sensors 23(13):5941. https:\/\/doi.org\/10.3390\/s23135941","journal-title":"Sensors"},{"key":"490_CR11","doi-asserted-by":"publisher","first-page":"40682","DOI":"10.1109\/ACCESS.2024.3376400","volume":"12","author":"M Ali","year":"2024","unstructured":"Ali M, Shahroz M, Mushtaq MF, Alfarhood S, Safran M, Ashraf I (2024) Hybrid machine learning model for efficient botnet attack detection in IoT environment. IEEE Access 12:40682\u201340699. https:\/\/doi.org\/10.1109\/ACCESS.2024.3376400","journal-title":"IEEE Access"},{"issue":"1","key":"490_CR12","doi-asserted-by":"publisher","first-page":"31","DOI":"10.22441\/sinergi.2024.1.004","volume":"28","author":"S Susanto","year":"2023","unstructured":"Susanto S, Stiawan D, Arifin MAS, Idris MY, Budiarto R (2023) Effective and efficient approach in IoT botnet detection. SINERGI 28(1):31. https:\/\/doi.org\/10.22441\/sinergi.2024.1.004","journal-title":"SINERGI"},{"key":"490_CR13","doi-asserted-by":"publisher","unstructured":"Sutheekshan B, Basheer S, Thangavel G, Sharma OP (2024) Evolution of malware targeting IoT devices and botnet formation. In: 2024 IEEE international conference on computing, power and communication technologies (IC2PCT). IEEE. https:\/\/doi.org\/10.1109\/ic2pct60090.2024.10486705","DOI":"10.1109\/ic2pct60090.2024.10486705"},{"key":"490_CR14","doi-asserted-by":"publisher","unstructured":"Fortune DC, Mathurin SS, Kalita S (2024) HTTP-based peer-to-peer botnet detection using a machine learning bagging classifier. In: 2024 2nd international conference on disruptive technologies (ICDT). IEEE. https:\/\/doi.org\/10.1109\/icdt61202.2024.10489499","DOI":"10.1109\/icdt61202.2024.10489499"},{"key":"490_CR15","doi-asserted-by":"publisher","unstructured":"Sharma Y, Kumar V, Chaudhary H (2023) Attack detection on internet of things devices using machine learning techniques. In: 2023 7th international conference on intelligent computing and control systems (ICICCS). IEEE. https:\/\/doi.org\/10.1109\/iciccs56967.2023.10142701","DOI":"10.1109\/iciccs56967.2023.10142701"},{"key":"490_CR16","doi-asserted-by":"publisher","unstructured":"Zaheer A, Tahir S, Almufareh MF, Hamid B (2023) A hybrid model for botnet detection using machine learning. In: 2023 International conference on business analytics for technology and security (ICBATS). IEEE. https:\/\/doi.org\/10.1109\/icbats57792.2023.10111161","DOI":"10.1109\/icbats57792.2023.10111161"},{"key":"490_CR17","doi-asserted-by":"publisher","DOI":"10.31449\/inf.v47i6.4668","author":"AD Khaleefah","year":"2023","unstructured":"Khaleefah AD, Al-Mashhadi HM (2023) Detection of IoT botnet cyber attacks using machine learning. Informatica. https:\/\/doi.org\/10.31449\/inf.v47i6.4668","journal-title":"Informatica"},{"key":"490_CR18","doi-asserted-by":"publisher","unstructured":"Esha H, Hadimani BS, Devika SP, Shanthala PT, Bhavana R (2023) IoT botnet creation and detection using machine learning. In: 2023 International conference on advancement in computation and computer technologies (InCACCT). IEEE. https:\/\/doi.org\/10.1109\/incacct57535.2023.10141717","DOI":"10.1109\/incacct57535.2023.10141717"},{"key":"490_CR19","doi-asserted-by":"publisher","unstructured":"Htwe CS, Su Thwin MM, Thant YM (2023) Malware attack detection using machine learning methods for IoT smart devices. In: 2023 IEEE conference on computer applications (ICCA). IEEE. https:\/\/doi.org\/10.1109\/icca51723.2023.10181535","DOI":"10.1109\/icca51723.2023.10181535"},{"key":"490_CR20","doi-asserted-by":"publisher","unstructured":"Jilani AK, Ahmad F, Khan MAR, Jabeen A (2023) Machine learning based framework for attack detection on IoT devices. In: 2023 IEEE 8th international conference on engineering technologies and applied sciences (ICETAS). IEEE. https:\/\/doi.org\/10.1109\/icetas59148.2023.10346563","DOI":"10.1109\/icetas59148.2023.10346563"},{"key":"490_CR21","doi-asserted-by":"publisher","unstructured":"Sharma A, Babbar H (2023) Machine learning-based anomaly detection in the internet of things. In: 2023 3rd Asian conference on innovation in technology (ASIANCON). IEEE. https:\/\/doi.org\/10.1109\/asiancon58793.2023.10270100","DOI":"10.1109\/asiancon58793.2023.10270100"},{"key":"490_CR22","doi-asserted-by":"publisher","unstructured":"Arshad S, Zanib R, Akram A, Haider A, Saeed T, Raza MS (2023) ML-IBotD: machine learning based intelligent botnet detection. In: 2023 3rd international conference on artificial intelligence (ICAI). IEEE. https:\/\/doi.org\/10.1109\/icai58407.2023.10136647","DOI":"10.1109\/icai58407.2023.10136647"},{"key":"490_CR23","doi-asserted-by":"publisher","unstructured":"Gangone A, Bala B, Gangone S (2023) The deep learning and machine learning methods for botnet identification in the Internet of Things. In: 2023 6th international conference on contemporary computing and informatics (IC3I). IEEE. https:\/\/doi.org\/10.1109\/ic3i59117.2023.10397881","DOI":"10.1109\/ic3i59117.2023.10397881"},{"key":"490_CR24","doi-asserted-by":"publisher","unstructured":"Hossain MA, Saiful Islam M (2023) An ensemble-based machine learning approach for botnet-based DDoS attack detection. In: 2023 IEEE international conference on telecommunications and photonics (ICTP). IEEE. https:\/\/doi.org\/10.1109\/ictp60248.2023.10490528","DOI":"10.1109\/ictp60248.2023.10490528"},{"key":"490_CR25","doi-asserted-by":"publisher","unstructured":"Ramesh RB, Thangaraj SJJ (2023) Analyzing and detecting botnet attacks using anomaly detection with machine learning. In: 2023 5th international conference on inventive research in computing applications (ICIRCA). IEEE. https:\/\/doi.org\/10.1109\/icirca57980.2023.10220903","DOI":"10.1109\/icirca57980.2023.10220903"},{"key":"490_CR26","doi-asserted-by":"publisher","unstructured":"Raju VSA, B S (2023). Network intrusion detection for IoT-botnet attacks using ML algorithms. In: 2023 7th international conference on computation system and information technology for sustainable solutions (CSITSS). IEEE. https:\/\/doi.org\/10.1109\/csitss60515.2023.10334188","DOI":"10.1109\/csitss60515.2023.10334188"},{"key":"490_CR27","doi-asserted-by":"publisher","first-page":"1405","DOI":"10.1016\/j.procs.2023.01.119","volume":"218","author":"RSS Moorthy","year":"2023","unstructured":"Moorthy RSS, Nathiya N (2023) Botnet detection using artificial intelligence. Procedia Comput Sci 218:1405\u20131413. https:\/\/doi.org\/10.1016\/j.procs.2023.01.119","journal-title":"Procedia Comput Sci"},{"key":"490_CR28","doi-asserted-by":"publisher","unstructured":"Arya M, Arya S, Arya S (2023) An evaluation of real-time malware detection in IoT devices: comparison of machine learning algorithms with RapidMiner. In: 2023 IEEE international conference on electro information technology (eIT). IEEE. https:\/\/doi.org\/10.1109\/eit57321.2023.10187265","DOI":"10.1109\/eit57321.2023.10187265"},{"key":"490_CR29","doi-asserted-by":"publisher","unstructured":"Baja Y, Chougdali K, Kobbane A (2023) Improving IoT botnet detection using ensemble learning. In: 2023 6th international conference on advanced communication technologies and networking (CommNet). IEEE. https:\/\/doi.org\/10.1109\/commnet60167.2023.10365268","DOI":"10.1109\/commnet60167.2023.10365268"},{"key":"490_CR30","doi-asserted-by":"publisher","first-page":"107525","DOI":"10.1016\/j.compeleceng.2021.107525","volume":"97","author":"GL Nguyen","year":"2022","unstructured":"Nguyen GL, Dumba B, Ngo Q-D, Le H-V, Nguyen TN (2022) A collaborative approach to early detection of IoT botnet. Comput Electr Eng 97:107525. https:\/\/doi.org\/10.1016\/j.compeleceng.2021.107525","journal-title":"Comput Electr Eng"},{"key":"490_CR31","doi-asserted-by":"publisher","unstructured":"Alissa K, Alyas T, Zafar K, Abbas Q, Tabassum N, Sakib S (2022) Botnet attack detection in IoT using machine learning. In: Rehman AU (ed) Computational intelligence and neuroscience, vol 2022. Hindawi Limited, pp 1\u201314. https:\/\/doi.org\/10.1155\/2022\/4515642","DOI":"10.1155\/2022\/4515642"},{"key":"490_CR32","doi-asserted-by":"publisher","unstructured":"Jeelani F, Rai DS, Maithani A, Gupta S (2022) The detection of IoT botnet using machine learning on IoT-23 dataset. In: 2022 2nd international conference on innovative practices in technology and management (ICIPTM). IEEE. https:\/\/doi.org\/10.1109\/iciptm54933.2022.9754187","DOI":"10.1109\/iciptm54933.2022.9754187"},{"key":"490_CR33","doi-asserted-by":"publisher","unstructured":"Mashaleh AS, Binti Ibrahim NF, Alauthman M, Almomani A (2022) A proposed framework for early detection IoT botnet. In: 2022 International Arab conference on information technology (ACIT). IEEE. https:\/\/doi.org\/10.1109\/acit57182.2022.9994166","DOI":"10.1109\/acit57182.2022.9994166"},{"key":"490_CR34","doi-asserted-by":"publisher","unstructured":"Puri V, Kataria A, Solanki VK, Rani S (2022) AI-based botnet attack classification and detection in IoT devices. In: 2022 IEEE international conference on machine learning and applied network technologies (ICMLANT). IEEE. https:\/\/doi.org\/10.1109\/icmlant56191.2022.9996464","DOI":"10.1109\/icmlant56191.2022.9996464"},{"key":"490_CR35","doi-asserted-by":"publisher","unstructured":"Deshmukh A, Sreenath N, Tyagi AK, Jathar S (2022) Internet of things based smart environment: threat analysis, open issues, and a way forward to future. In 2022 International conference on computer communication and informatics (ICCCI). IEEE. https:\/\/doi.org\/10.1109\/iccci54379.2022.9740741","DOI":"10.1109\/iccci54379.2022.9740741"},{"key":"490_CR36","doi-asserted-by":"publisher","unstructured":"Raghavendra M, Chen Z (2022) Detecting IoT botnets on IoT edge devices. In: 2022 IEEE international conference on communications workshops (ICC Workshops). IEEE. https:\/\/doi.org\/10.1109\/iccworkshops53468.2022.9814555","DOI":"10.1109\/iccworkshops53468.2022.9814555"},{"key":"490_CR37","doi-asserted-by":"publisher","DOI":"10.1016\/j.cose.2022.102693","volume":"117","author":"A Kumar","year":"2022","unstructured":"Kumar A, Shridhar M, Swaminathan S, Lim TJ (2022) Machine learning-based early detection of IoT botnets using network-edge traffic. Comput Secur 117:102693. https:\/\/doi.org\/10.1016\/j.cose.2022.102693","journal-title":"Comput Secur"},{"issue":"6","key":"490_CR38","doi-asserted-by":"publisher","first-page":"9610","DOI":"10.1109\/JIOT.2023.3324053","volume":"11","author":"M Al-Fawa\u2019reh","year":"2024","unstructured":"Al-Fawa\u2019reh M, Abu-Khalaf J, Szewczyk P, Kang JJ (2024) MalBoT-DRL: malware botnet detection using deep reinforcement learning in IoT networks. IEEE Internet Things J 11(6):9610\u20139629. https:\/\/doi.org\/10.1109\/JIOT.2023.3324053","journal-title":"IEEE Internet Things J"},{"issue":"10","key":"490_CR39","doi-asserted-by":"publisher","DOI":"10.1016\/j.jksuci.2023.101820","volume":"35","author":"A Nazir","year":"2023","unstructured":"Nazir A, He J, Zhu N, Wajahat A, Ma X, Ullah F, Qureshi S, Pathan MS (2023) Advancing IoT security: a systematic review of machine learning approaches for the detection of IoT botnets. J King Saud Univ-Comput Inf Sci 35(10):101820. https:\/\/doi.org\/10.1016\/j.jksuci.2023.101820","journal-title":"J King Saud Univ-Comput Inf Sci"},{"issue":"10","key":"490_CR40","doi-asserted-by":"publisher","first-page":"8455","DOI":"10.1109\/JIOT.2022.3229463","volume":"10","author":"CA Fadhilla","year":"2023","unstructured":"Fadhilla CA, Alfikri MD, Kaliski R (2023) Lightweight meta-learning botnet attack detection. IEEE Internet Things J 10(10):8455\u20138466. https:\/\/doi.org\/10.1109\/JIOT.2022.3229463","journal-title":"IEEE Internet Things J"},{"issue":"6","key":"490_CR41","doi-asserted-by":"publisher","DOI":"10.3390\/fi15060210","volume":"15","author":"A Koirala","year":"2023","unstructured":"Koirala A, Bista R, Ferreira JC (2023) Enhancing IoT device security through network attack data analysis using machine learning algorithms. Future Internet 15(6):210. https:\/\/doi.org\/10.3390\/fi15060210","journal-title":"Future Internet"},{"issue":"1","key":"490_CR42","doi-asserted-by":"publisher","DOI":"10.3390\/jsan11010018","volume":"11","author":"Q Abu Al-Haija","year":"2022","unstructured":"Abu Al-Haija Q, Al-Dala\u2019ien M (2022) Elba-IoT: an ensemble learning model for botnet attack detection in IoT networks. J Sens Actuator Netw 11(1):18. https:\/\/doi.org\/10.3390\/jsan11010018","journal-title":"J Sens Actuator Netw"},{"key":"490_CR43","doi-asserted-by":"publisher","first-page":"99166","DOI":"10.1109\/ACCESS.2021.3094183","volume":"9","author":"A Alharbi","year":"2021","unstructured":"Alharbi A, Alsubhi K (2021) Botnet detection approach using graph-based machine learning. IEEE Access 9:99166\u201399180. https:\/\/doi.org\/10.1109\/ACCESS.2021.3094183","journal-title":"IEEE Access"},{"key":"490_CR44","doi-asserted-by":"publisher","first-page":"91038","DOI":"10.1109\/ACCESS.2021.3092054","volume":"9","author":"M Panda","year":"2021","unstructured":"Panda M, Mousa AAA, Hassanien AE (2021) Developing an efficient feature engineering and machine learning model for detecting IoT-botnet cyber attacks. IEEE Access 9:91038\u201391052. https:\/\/doi.org\/10.1109\/ACCESS.2021.3092054","journal-title":"IEEE Access"},{"issue":"6","key":"490_CR45","doi-asserted-by":"publisher","first-page":"4944","DOI":"10.1109\/JIOT.2020.3034156","volume":"8","author":"SI Popoola","year":"2021","unstructured":"Popoola SI, Adebisi B, Hammoudeh M, Gui G, Gacanin H (2021) Hybrid deep learning for botnet attack detection in the Internet-of-Things networks. IEEE Internet Things J 8(6):4944\u20134956. https:\/\/doi.org\/10.1109\/JIOT.2020.3034156","journal-title":"IEEE Internet Things J"},{"key":"490_CR46","doi-asserted-by":"publisher","first-page":"48753","DOI":"10.1109\/ACCESS.2021.3060778","volume":"9","author":"WNH Ibrahim","year":"2021","unstructured":"Ibrahim WNH et al (2021) Multilayer framework for botnet detection using machine learning algorithms. IEEE Access 9:48753\u201348768. https:\/\/doi.org\/10.1109\/ACCESS.2021.3060778","journal-title":"IEEE Access"},{"key":"490_CR47","doi-asserted-by":"publisher","first-page":"160391","DOI":"10.1109\/ACCESS.2021.3130714","volume":"9","author":"B Stephens","year":"2021","unstructured":"Stephens B, Shaghaghi A, Doss R, Kanhere SS (2021) Detecting internet of things bots: a comparative study. IEEE Access 9:160391\u2013160401. https:\/\/doi.org\/10.1109\/ACCESS.2021.3130714","journal-title":"IEEE Access"},{"issue":"19","key":"490_CR48","doi-asserted-by":"publisher","DOI":"10.3390\/app10197009","volume":"10","author":"J Kim","year":"2020","unstructured":"Kim J, Shim M, Hong S, Shin Y, Choi E (2020) Intelligent detection of IoT botnets using machine learning and deep learning. Appl Sci 10(19):7009. https:\/\/doi.org\/10.3390\/app10197009","journal-title":"Appl Sci"},{"issue":"2","key":"490_CR49","doi-asserted-by":"publisher","first-page":"551","DOI":"10.3390\/iot1020030","volume":"1","author":"DW Fernando","year":"2020","unstructured":"Fernando DW, Komninos N, Chen T (2020) A study on the evolution of ransomware detection using machine learning and deep learning techniques. IoT 1(2):551\u2013604. https:\/\/doi.org\/10.3390\/iot1020030","journal-title":"IoT"},{"issue":"1","key":"490_CR50","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1109\/TNSM.2020.2972405","volume":"17","author":"AA Daya","year":"2020","unstructured":"Daya AA, Salahuddin MA, Limam N, Boutaba R (2020) BotChase: graph-based bot detection using machine learning. IEEE Trans Netw Serv Manag 17(1):15\u201329. https:\/\/doi.org\/10.1109\/TNSM.2020.2972405","journal-title":"IEEE Trans Netw Serv Manag"},{"key":"490_CR51","unstructured":"CIC_IOT_Dataset (2023) Canadian institute of cyber security IoT dataset 2023. [Online] Available: http:\/\/205.174.165.80\/IOTDataset\/CIC_IOT_Dataset2023\/Dataset\/. Accessed May 2024"}],"container-title":["Memetic Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12293-025-00490-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s12293-025-00490-2","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12293-025-00490-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,30]],"date-time":"2026-03-30T09:47:39Z","timestamp":1774864059000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s12293-025-00490-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,1,29]]},"references-count":51,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2026,3]]}},"alternative-id":["490"],"URL":"https:\/\/doi.org\/10.1007\/s12293-025-00490-2","relation":{},"ISSN":["1865-9284","1865-9292"],"issn-type":[{"value":"1865-9284","type":"print"},{"value":"1865-9292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,1,29]]},"assertion":[{"value":"6 April 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 December 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 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 no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"13"}}