Multimodal Reasoning with LLM for Encrypted Traffic Interpretation: A Benchmark

• URL: http://arxiv.org/abs/2604.08140v1
• Date: Thu, 09 Apr 2026 11:56:28 GMT
• Title: Multimodal Reasoning with LLM for Encrypted Traffic Interpretation: A Benchmark
• Abstract summary: This paper proposes an end-to-end traffic-language representation framework (mmTraffic), a multimodal reasoning architecture bridging physical traffic encoding and semantic interpretation.<n>By a perception-centered traffic encoder and a cognition-centered LLM generator, mmTraffic achieves refined traffic interpretation with guaranteed category prediction.
• Score: 12.257114438374975
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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) benchmark for the first time, combining raw bytes with structured expert annotations. BGTD provides necessary behavioral features and verifiable chains of evidence for multimodal reasoning towards explainable encrypted traffic interpretation. Built upon BGTD, this paper proposes an end-to-end traffic-language representation framework (mmTraffic), a multimodal reasoning architecture bridging physical traffic encoding and semantic interpretation. In order to alleviate modality interference and generative hallucinations, mmTraffic adopts a jointly-optimized perception-cognition architecture. By incorporating a perception-centered traffic encoder and a cognition-centered LLM generator, mmTraffic achieves refined traffic interpretation with guaranteed category prediction. Extensive experiments demonstrate that mmTraffic autonomously generates high-fidelity, human-readable, and evidence-grounded traffic interpretation reports, while maintaining highly competitive classification accuracy comparing to specialized unimodal model (e.g., NetMamba). The source code is available at https://github.com/lgzhangzlg/Multimodal-Reasoning-with-LLM-for-Encrypted-Traffic-Interpretation-A-B enchmark

Related papers

• TDDM-Melatt: A Decoupled Memory and Diffusion Framework for Generalizable Encrypted Traffic Classification [8.96087505726163]
  We propose a disentangled memory-based traffic classification framework with diffusion-based data augmentation.<n>We design Melatt, a memory-decoupled traffic representation model, which employs Competitive Gating Long Short-Term Memory (CG-LSTM) to construct the encoder and decoder.<n>Under strict flow-level splitting and anonymization, TDDM-Melatt outperforms 6 basic classification models and 6 SOTA representation learning models.
  arXiv  Detail & Related papers  (2026-08-31T13:11:52Z)
• Traffic-CBM: A Structurally Interpretable Multimodal Framework for Encrypted Traffic Classification [5.591206695889022]
  We propose Traffic-CBM, a structurally interpretable multimodal framework for encrypted traffic classification.<n>Instead of directly fusing heterogeneous traffic signals into a black-box representation, Traffic-CBM organizes them into a unified hierarchical concept space.<n>Results show that Traffic-CBM achieves competitive and balanced classification performance while providing a clearer structural interpretation interface.
  arXiv  Detail & Related papers  (2026-06-29T07:44:56Z)
• OneDrive: Unified Multi-Paradigm Driving with Vision-Language-Action Models [69.2503510410147]
  We present a unified autonomous driving framework built upon a pretrained VLM.<n>We demonstrate that pretrained VLM attention exhibits strong transferability beyond pure language modeling.<n>Experiments on end-to-end autonomous driving benchmarks demonstrate state-of-the-art performance.
  arXiv  Detail & Related papers  (2026-04-20T07:50:00Z)
• TrafficMoE: Heterogeneity-aware Mixture of Experts for Encrypted Traffic Classification [12.748275751587363]
  Encrypted traffic classification is a critical task for network security.<n>The one-size-fits-all static design is inherently flawed.<n>We propose TrafficMoE, a framework that breaks through the bottleneck of static modeling.
  arXiv  Detail & Related papers  (2026-03-31T10:05:54Z)
• Perception-Aware Multimodal Spatial Reasoning from Monocular Images [57.42071289037214]
  spatial reasoning from monocular images is essential for autonomous driving.<n>Current Vision-Language Models (VLMs) still struggle with fine-grained geometric perception.<n>We propose a perception-aware multimodal reasoning framework that equipsVLMs with explicit object-centric grounding ability.
  arXiv  Detail & Related papers  (2026-03-07T02:05:12Z)
• ReGAIN: Retrieval-Grounded AI Framework for Network Traffic Analysis [5.887997322139195]
  ReGAIN is a framework that combines traffic summarization, retrieval-augmented generation (RAG), and Large Language Model (LLM) reasoning for transparent and accurate network traffic analysis.<n> evaluated on ICMP ping flood and TCP SYN flood traces from the real-world traffic dataset.
  arXiv  Detail & Related papers  (2025-12-23T00:16:14Z)
• METER: Multi-modal Evidence-based Thinking and Explainable Reasoning -- Algorithm and Benchmark [48.78602579128459]
  We introduce METER, a unified benchmark for interpretable forgery detection spanning images, videos, audio, and audio-visual content.<n>Our dataset comprises four tracks, each requiring not only real-vs-fake classification but also evidence-chain-based explanations.
  arXiv  Detail & Related papers  (2025-07-22T03:42:51Z)
• MIETT: Multi-Instance Encrypted Traffic Transformer for Encrypted Traffic Classification [59.96233305733875]
  Classifying traffic is essential for detecting security threats and optimizing network management.<n>We propose a Multi-Instance Encrypted Traffic Transformer (MIETT) to capture both token-level and packet-level relationships.<n>MIETT achieves results across five datasets, demonstrating its effectiveness in classifying encrypted traffic and understanding complex network behaviors.
  arXiv  Detail & Related papers  (2024-12-19T12:52:53Z)
• Cross-domain Multi-step Thinking: Zero-shot Fine-grained Traffic Sign Recognition in the Wild [49.20086587208214]
  Cross-domain Multi-step Thinking (CdMT) proposed to improve zero-shot fine-grained traffic sign recognition (TSR) performance in the wild.<n>CdMT is independent of training data and requires only simple and uniform instructions, enabling it to achieve cross-country TSR.<n> framework achieved superior performance compared with other state-of-the-art methods on five datasets.
  arXiv  Detail & Related papers  (2024-09-03T02:08:47Z)
• Lens: A Foundation Model for Network Traffic [19.3652490585798]
  Lens is a foundation model for network traffic that leverages the T5 architecture to learn the pre-trained representations from large-scale unlabeled data. We design a novel loss that combines three distinct tasks: Masked Span Prediction (MSP), Packet Order Prediction (POP), and Homologous Traffic Prediction (HTP)
  arXiv  Detail & Related papers  (2024-02-06T02:45:13Z)
• TFE-GNN: A Temporal Fusion Encoder Using Graph Neural Networks for Fine-grained Encrypted Traffic Classification [35.211600580761726]
  We propose a byte-level traffic graph construction approach based on point-wise mutual information (PMI) and a model named Temporal Fusion. In particular, we design a dual embedding layer, a GNN-based traffic graph encoder as well as a cross-gated feature fusion mechanism. The experimental results on two real datasets demonstrate that TFE-GNN outperforms multiple state-of-the-art methods in fine-grained encrypted traffic classification tasks.
  arXiv  Detail & Related papers  (2023-07-31T14:32:40Z)
• Object Segmentation by Mining Cross-Modal Semantics [68.88086621181628]
  We propose a novel approach by mining the Cross-Modal Semantics to guide the fusion and decoding of multimodal features. Specifically, we propose a novel network, termed XMSNet, consisting of (1) all-round attentive fusion (AF), (2) coarse-to-fine decoder (CFD), and (3) cross-layer self-supervision.
  arXiv  Detail & Related papers  (2023-05-17T14:30:11Z)
• Guiding the PLMs with Semantic Anchors as Intermediate Supervision: Towards Interpretable Semantic Parsing [57.11806632758607]
  We propose to incorporate the current pretrained language models with a hierarchical decoder network. By taking the first-principle structures as the semantic anchors, we propose two novel intermediate supervision tasks. We conduct intensive experiments on several semantic parsing benchmarks and demonstrate that our approach can consistently outperform the baselines.
  arXiv  Detail & Related papers  (2022-10-04T07:27:29Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.