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A lightweight spatiotemporal entropy-based detector for DDoS attacks using a constrained second-order Exponentially Weighted Moving Average threshold that jointly tracks entropy trend and volatility is proposed.
A cross-domain confidence-fusion framework that leverages lightweight edge-side messages to calibrate aggregation-controller decisions without sharing raw traffic data is proposed that is non-intrusive, communication-efficient, and incrementally deployable.
This work introduces Audio BERT (AuB), a trainable model that constructs token embeddings from discrete codebooks and aggregates them into speaker-sensitive representations, and proposes SpInv, a two-stage inversion method built on AuB to recover embeddings in the space of an attacker-specified speaker encoder.
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