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Paper Ugh35Hn6A8Eb1F2E Explanation Page
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Figures Explained
Fig. 1. Comparison of traffic analysis paradigms. (a) Traditional classification methods that act as a u201cblack boxu201d, providing only a label and low-level feature weights that lack operational value. (b) Our proposed multimodal reasoning framework, composed of a Traffic Perception Encoder and a Cognitive LLM, instructed by Byte-Grounded Knowledge, generating an evidence-grounded report with human-understandable reasoning and executable insights.
Fig. 1. Comparison of traffic analysis paradigms. (a) Traditional classification methods that act as a u201cblack boxu201d, providing only a label and low-level feature weights that lack operational value. (b) Our proposed multimodal reasoning framework, composed of a Traffic Perception Encoder and a Cognitive LLM, instructed by Byte-Grounded Knowledge, generating an evidence-grounded report with human-understandable reasoning and executable insights.
Fig. 2. Pipeline of developing BGTD dataset: (a) session extraction and class balancing from raw PCAP files, (b) fixed-length 10 u00d7 160 NPY array generation via priority-based packet sampling, and (c) LLM-assisted ground-truth synthesis using Claude Opus-4.6 prompted as a senior network security expert.
Fig. 2. Pipeline of developing BGTD dataset: (a) session extraction and class balancing from raw PCAP files, (b) fixed-length 10 u00d7 160 NPY array generation via priority-based packet sampling, and (c) LLM-assisted ground-truth synthesis using Claude Opus-4.6 prompted as a senior network security expert.
Fig. 3. Statistical overview of the BGTD dataset.
Fig. 3. Statistical overview of the BGTD dataset.
Fig. 4. Overview of the mmTraffic framework. (a) The frozen traffic encoder Tu03b8 extracts high-dimensional features from raw traffic data. (b) The linear connector Cu03c9 projects traffic features into the LLM token space, with the CGHF mechanism injecting a class-aware anchor token into the input sequence. (c) The LLM Gu03d5 autoregressively generates a structured forensic report containing behavioral traits, evidence chain, and diagnostic description.
Fig. 4. Overview of the mmTraffic framework. (a) The frozen traffic encoder Tu03b8 extracts high-dimensional features from raw traffic data. (b) The linear connector Cu03c9 projects traffic features into the LLM token space, with the CGHF mechanism injecting a class-aware anchor token into the input sequence. (c) The LLM Gu03d5 autoregressively generates a structured forensic report containing behavioral traits, evidence chain, and diagnostic description.
Fig. 5. Analysis on Structural Consistency Metrics. The semantic-priority constraints in mmTraffic ensure high logical rigor.
Fig. 5. Analysis on Structural Consistency Metrics. The semantic-priority constraints in mmTraffic ensure high logical rigor.
Fig. 6. Ablation analysis on ISCX-Tor-2016 and ISCXVPN2016, with respect to the classification and generation metrics for four variants from V1 to V4.
Fig. 6. Ablation analysis on ISCX-Tor-2016 and ISCXVPN2016, with respect to the classification and generation metrics for four variants from V1 to V4.