Adaptive AI Framework Powered by Machine Learning for Smart Network Intrusion Detection Systems

Network intrusion detection is a fundamental part of cyber defense, however, signature-based IDS are struggling to detect unknown attacks and many ML based IDS are trained once on static data and then degrade as traffic and attack behavior evolve.This study proposes an adaptive framework for network intrusion detection that includes intelligent feature selection, a supervised ensemble classifier, an unsupervised anomaly detector, a concept-drift monitor, and a drift-triggered model-update policy with a validation gate to limit unnecessary retraining.The primary methods used in this framework include Random Forest, Gradient Boosting, Isolation Forest, an incremental classifier, and ADWIN and Page-Hinkley drift detectors.The framework is formally defined, including the ensemble decision rule, evaluation metrics, drift statistic, and update criterion, which are explicitly separated from standard formulations.Evaluation of this framework will be conducted using chronologically ordered splits of CICIDS2017 and UNSW-NB15 datasets, along with synthetic drift injection, ablation studies, and statistical testing.The evaluation results will include macro-F1, recall, false-positive rate, detection latency, update frequency, and computational cost.As of now, no experiments have been completed; therefore, the framework is proposed and its evaluation is planned.The expected contribution is a reproducible design and evaluation protocol for determining if drift-aware adaptation enhances the robustness of ML based intrusion detection under changing traffic conditions, and potentially applied to Security Operations environments.