X-STREAM-IDS: An Explainable Streaming-Aware Deep Learning based Intrusion Detection System for IoT

The proposed X-STREAM-IDS makes the following key contributions:
1. Develops a dataset-aware multi-file attack taxonomy for the BoT-IoT dataset to support controlled multi-class intrusion labeling.
2. Introduces a robust feature encoding mechanism for handling heterogeneous and noisy IoT traffic.
3. Implements mini-batch online learning with noise-based regularization to simulate streaming traffic and reduce overfitting.
4. Integrates SHAP and LIME to provide interpretable and transparent intrusion detection decisions.