Multimodal Reasoning with LLM for Encrypted Traffic Interpretation: A Benchmark
Network traffic, as a key media format, is crucial for ensuring security and communications in modern internet infrastructure. While existing methods offer excellent performance, they face two key bottlenecks: (1) They fail to capture multidimensional semantics beyond unimodal sequence patterns. (2) Their black box property, i.e., providing only category labels, lacks an auditable reasoning process. We identify a key factor that existing network traffic datasets are primarily designed for classification and inherently lack rich semantic annotations, failing to generate human-readable evidence report. To address data scarcity, this paper proposes a Byte-Grounded Traffic Description (BGTD) ...
| Authors | Longgang Zhang, Xiaowei Fu, Fuxiang Huang et al. |
|---|---|
| Primary category | cs.CR |
| Published | 9 April 2026 |
| Last updated at source | 9 April 2026 |
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