Multimodal LLM Reasoning for Encrypted Network Traffic Interpretation with Audit Trails

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Introduces a benchmark and method for using multimodal LLMs to interpret encrypted network traffic, moving beyond black-box classification labels to provide auditable reasoning processes. The approach captures multidimensional semantics beyond unimodal sequence patterns that current methods miss. Addresses the fundamental limitation that existing traffic analysis tools provide category labels without explanation, which is inadequate for security operations requiring justification.