Mohammed Nagah Amr

Sequential submodular feature-sample selection for lightweight and update-efficient IoT intrusion detection

The Sequential Submodular Feature-Sample Selection (SSFSS) framework is proposed, a theoretically grounded approach that sequentially optimizes the feature and sample spaces to reduce training cost while preserving detection fidelity, and is positioned as an efficient update-aware preprocessing framework whose measured...