| Summary: | Data drift caused due to network changes, new device additions, or model degradation alters the patterns learned by ML/DL models, resulting in poor classification performance. This creates the need for a generalized, drift-resilient model that can learn without retraining in dynamic environments. To maintain high accuracy, such a model must classify previously unseen IoT devices effectively. In this study, we propose a three-tier incremental architecture (CNN-PN-RF) combining Convolutional Neural Network (CNN) for feature extraction, Prototypical Network (PN) for class embedding, and Random Forest (RF) for robust classification. The model utilizes six aggregated diverse IoT datasets.Two similarly structured datasets (Dataset 1 and Dataset 2) were created from it, differing in training-testing splits, with some device CSV files withheld to test on unseen classification. Phase 1 employs a stand-alone CNN-based model with L2 regularization, dropout, and early stopping, achieving 70.96% accuracy. Phase 2 integrates CNN with RF, using SMOTE for class balancing and PCA for dimensionality reduction, attaining 83.79% accuracy. Phase 3 introduces PN to finalize the CNN-PN-RF model, enhancing classification issue of feature clustering, intra-class separability, and small-class support. Final accuracy, precision, recall, and F1-score were 99.56%, 99.66%, 99.56%, and 99.59% for Dataset 1, and 99.80% for all metrics on Dataset 2. The model was compared with state-of-the-art approaches and validated on unseen IoT subsets of both datasets, showing better generalization capability.
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