SATA generates realistic new traffic patterns absent from training data and boosts open-world website fingerprinting accuracy by 90.81% and AUROC by 48.37% through protocol-based semantic augmentation and cross-layer feature alignment.
Content delivery networks: State of the art, trends, and future roadmap
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DAT combines a small-large model cascade with fine-tuning and bandwidth-aware multi-stream transmission to deliver high-accuracy event recognition and low-latency alerts for video streams in edge-cloud systems.
citing papers explorer
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More Than Meets the Eye: A Semantics-Aware Traffic Augmentation Framework for Generalizable Website Fingerprinting
SATA generates realistic new traffic patterns absent from training data and boosts open-world website fingerprinting accuracy by 90.81% and AUROC by 48.37% through protocol-based semantic augmentation and cross-layer feature alignment.
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DAT: Dual-Aware Adaptive Transmission for Efficient Multimodal LLM Inference in Edge-Cloud Systems
DAT combines a small-large model cascade with fine-tuning and bandwidth-aware multi-stream transmission to deliver high-accuracy event recognition and low-latency alerts for video streams in edge-cloud systems.