BiLCNet : BiLSTM-Conformer Network for Encrypted Traffic Classification with 5G SA Physical Channel Records

• URL: http://arxiv.org/abs/2509.17495v1
• Date: Mon, 22 Sep 2025 08:27:11 GMT
• Title: BiLCNet : BiLSTM-Conformer Network for Encrypted Traffic Classification with 5G SA Physical Channel Records
• Authors: Ke Ma, Jialiang Lu, Philippe Martins,
• Abstract summary: We develop a preprocessing pipeline to transform raw channel records into structured representations.<n>We propose a novel hybrid architecture: BiLSTM-Conformer Network (BiLCNet), which integrates the sequential modeling capability of BiLSTM with the spatial feature extraction strength of Conformer blocks.<n>Our model achieves a classification accuracy of 93.9%, outperforming a series of conventional machine learning and deep learning algorithms.
• Score: 3.4949103362575573
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Accurate and efficient traffic classification is vital for wireless network management, especially under encrypted payloads and dynamic application behavior, where traditional methods such as port-based identification and deep packet inspection (DPI) are increasingly inadequate. This work explores the feasibility of using physical channel data collected from the air interface of 5G Standalone (SA) networks for traffic sensing. We develop a preprocessing pipeline to transform raw channel records into structured representations with customized feature engineering to enhance downstream classification performance. To jointly capture temporal dependencies and both local and global structural patterns inherent in physical channel records, we propose a novel hybrid architecture: BiLSTM-Conformer Network (BiLCNet), which integrates the sequential modeling capability of Bidirectional Long Short-Term Memory networks (BiLSTM) with the spatial feature extraction strength of Conformer blocks. Evaluated on a noise-limited 5G SA dataset, our model achieves a classification accuracy of 93.9%, outperforming a series of conventional machine learning and deep learning algorithms. Furthermore, we demonstrate its generalization ability under zero-shot transfer settings, validating its robustness across traffic categories and varying environmental conditions.

Related papers

• Wireless Traffic Prediction with Large Language Model [54.07581399989292]
  TIDES is a novel framework that captures spatial-temporal correlations for wireless traffic prediction.<n> TIDES achieves efficient adaptation to domain-specific patterns without incurring excessive training overhead.<n>Our results indicate that integrating spatial awareness into LLM-based predictors is the key to unlocking scalable and intelligent network management in future 6G systems.
  arXiv  Detail & Related papers  (2025-12-19T04:47:40Z)
• Resource Allocation in Hybrid Radio-Optical IoT Networks using GNN with Multi-task Learning [11.833896722352568]
  This paper addresses the problem of dual-technology scheduling in hybrid Internet of Things (IoT) networks that integrate Optical NeuralOWC and Radio Frequency (RF)<n>We propose a supervised multi-task learning architecture combining a two-stage Graph Embedding with Transformer (DGET) framework.<n>The proposed framework achieves near-optimal scheduling with over 90% classification accuracy, reduces computational complexity, and demonstrates higher robustness under partial channel observability.
  arXiv  Detail & Related papers  (2025-10-29T15:02:28Z)
• 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)
• Image Captioning via Dynamic Path Customization [100.15412641586525]
  We propose a novel Dynamic Transformer Network (DTNet) for image captioning, which dynamically assigns customized paths to different samples, leading to discriminative yet accurate captions. To validate the effectiveness of our proposed DTNet, we conduct extensive experiments on the MS-COCO dataset and achieve new state-of-the-art performance.
  arXiv  Detail & Related papers  (2024-06-01T07:23:21Z)
• TCCT-Net: Two-Stream Network Architecture for Fast and Efficient Engagement Estimation via Behavioral Feature Signals [58.865901821451295]
  We present a novel two-stream feature fusion "Tensor-Convolution and Convolution-Transformer Network" (TCCT-Net) architecture. To better learn the meaningful patterns in the temporal-spatial domain, we design a "CT" stream that integrates a hybrid convolutional-transformer. In parallel, to efficiently extract rich patterns from the temporal-frequency domain, we introduce a "TC" stream that uses Continuous Wavelet Transform (CWT) to represent information in a 2D tensor form.
  arXiv  Detail & Related papers  (2024-04-15T06:01:48Z)
• ELGC-Net: Efficient Local-Global Context Aggregation for Remote Sensing Change Detection [65.59969454655996]
  We propose an efficient change detection framework, ELGC-Net, which leverages rich contextual information to precisely estimate change regions. Our proposed ELGC-Net sets a new state-of-the-art performance in remote sensing change detection benchmarks. We also introduce ELGC-Net-LW, a lighter variant with significantly reduced computational complexity, suitable for resource-constrained settings.
  arXiv  Detail & Related papers  (2024-03-26T17:46:25Z)
• 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)
• Wavelet-Inspired Multiscale Graph Convolutional Recurrent Network for Traffic Forecasting [0.0]
  We propose a Graph Conal Recurrent Network (WavGCRN) which combines multiscale analysis (MSA)-based method with Deep Learning (DL)-based method. The proposed method can offer well-defined interpretability, powerful learning capability, and competitive forecasting performance on real-world traffic data sets.
  arXiv  Detail & Related papers  (2024-01-11T16:55:48Z)
• Real-time Traffic Classification for 5G NSA Encrypted Data Flows With Physical Channel Records [3.8998980344852856]
  classification of fifth-generation New-Radio (5G-NR) mobile network traffic is an emerging topic in the field of telecommunications. Traditional approaches such as Deep Packet Inspection (DPI) can not be directly applied to encrypted data flows. In this study, we examine the real-time encrypted 5G Non-Standalone (NSA) application-level traffic classification using physical channel records.
  arXiv  Detail & Related papers  (2023-07-15T09:32:34Z)
• PDFNet: Pointwise Dense Flow Network for Urban-Scene Segmentation [0.0]
  We propose a novel lightweight architecture named point-wise dense flow network (PDFNet) In PDFNet, we employ dense, residual, and multiple shortcut connections to allow a smooth gradient flow to all parts of the network. Our method significantly outperforms baselines in capturing small classes and in few-data regimes.
  arXiv  Detail & Related papers  (2021-09-21T10:39:46Z)
• Dynamic Slimmable Network [105.74546828182834]
  We develop a dynamic network slimming regime named Dynamic Slimmable Network (DS-Net) Our DS-Net is empowered with the ability of dynamic inference by the proposed double-headed dynamic gate. It consistently outperforms its static counterparts as well as state-of-the-art static and dynamic model compression methods.
  arXiv  Detail & Related papers  (2021-03-24T15:25:20Z)

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.