Fahad Alkamli, Morched Derbali and Tariq Ahmed
Adv. Artif. Intell. Mach. Learn., XX (XX):-
1. Fahad Alkamli: Department of Information TechnologyFaculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah, Saudi Arabia
2. Morched Derbali: Department of Computer Science, Faculty of Computing and Information Technology,King Abdulaziz University,Jeddah, Saudi Arabia
3. Tariq Ahmed: Department of Computer Science, Faculty of Computing and Information Technology,King Abdulaziz University,Jeddah, Saudi Arabia
DOI: 10.54364/AAIML.2026.62287
Article History: Received on: 12-Dec-25, Accepted on: 04-Mar-26, Published on: 11-Mar-26
Corresponding Author: Fahad Alkamli
Email: falkamli@stu.kau.edu.sa
Citation: Fahad Alkamli, et al. Honeypot-Driven Hybrid Continuous Learning Platform. Advances in Artificial Intelligence and Machine Learning. 2026. (Ahead of Print). https://dx.doi.org/10.54364/AAIML.2026.62287
This research paper introduces a hybrid continuous learning platform using honeypots that will improve proactive cyber threat detection in dynamic environments. The proposed system combines a monitored warm-up step and an incremental The platform makes use Confidence based filtering ensures that only trustworthy pseudo labels are used to update the model, which helps stabilize performance and reduces the risk of catastrophic forgetting. The paper has proven the hypothesis that incremental learning applied to real attack traffic offers quantifiable benefits over static batch learning. The findings show that a scalable, flexible and autonomous intrusion detection mechanism is practical and can improve itself over the long run.