{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,22]],"date-time":"2026-07-22T11:30:56Z","timestamp":1784719856530,"version":"3.55.0"},"reference-count":0,"publisher":"Advances in Artificial Intelligence and Machine Learning","issue":"02","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["AAIML"],"published-print":{"date-parts":[[2026]]},"abstract":"<jats:p>This research paper introduces a hybrid continuous learning platform using honeypots that\nwill improve proactive cyber threat detection in dynamic environments. The proposed system\ncombines a monitored warm-up step and an incremental semi- supervised learning route\nthat constantly adapts to actual web traffic that has been gathered using a honeypot. The\nplatform makes use of River framework and Multinomial Naive Bayes to process labeled\nand unlabeled HTTP request logs. The experimental findings across seventeen cycles of\nlearning depict a definite enhancement in AUC, accuracy, precision and F1 following a shift\nfrom supervised to semi-supervised training. Confidence based filtering ensures that only\ntrustworthy pseudo labels are used to update the model, which helps stabilize performance\nand reduces the risk of catastrophic forgetting. The paper has proven the hypothesis that\nincremental learning applied to real attack traffic offers quantifiable benefits over static batch\nlearning. The findings show that a scalable, flexible and autonomous intrusion detection\nmechanism is practical and can improve itself over the long run.<\/jats:p>","DOI":"10.54364\/aaiml.2026.62287","type":"journal-article","created":{"date-parts":[[2026,3,13]],"date-time":"2026-03-13T05:59:38Z","timestamp":1773381578000},"page":"5176-5197","source":"Crossref","is-referenced-by-count":1,"title":["Honeypot-Driven Hybrid Continuous Learning Platform for Proactive Cyber Threat Detection"],"prefix":"10.54364","volume":"06","author":[{"given":"Fahad","family":"Alkamli","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Morched","family":"Derbali","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tariq","family":"Mohamed Ahmed","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"32807","published-online":{"date-parts":[[2026]]},"container-title":["Advances in Artificial Intelligence and Machine Learning"],"original-title":[],"link":[{"URL":"https:\/\/www.oajaiml.com\/uploads\/archivepdf\/598362287.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T05:09:58Z","timestamp":1777612198000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.oajaiml.com\/uploads\/archivepdf\/598362287.pdf"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"references-count":0,"journal-issue":{"issue":"02","published-online":{"date-parts":[[2026]]},"published-print":{"date-parts":[[2026]]}},"URL":"https:\/\/doi.org\/10.54364\/aaiml.2026.62287","relation":{},"ISSN":["2582-9793"],"issn-type":[{"value":"2582-9793","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]}}}