Creates the BGTD benchmark and mmTraffic architecture to enable explainable multimodal interpretation of encrypted network traffic using LLMs.
Deep learning and pre- training technology for encrypted traffic classification: A comprehensive review
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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.
citing papers explorer
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Multimodal Reasoning with LLM for Encrypted Traffic Interpretation: A Benchmark
Creates the BGTD benchmark and mmTraffic architecture to enable explainable multimodal interpretation of encrypted network traffic using LLMs.
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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.