mlsanityData to 7 Oct 2026

Deep Learning for Encrypted Traffic Classification: An Overview

arXiv 1810.07906 ↗First posted 18 Oct 2018cs.NIcited by 27

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Abstract (arXiv)

Traffic classification has been studied for two decades and applied to a wide range of applications from QoS provisioning and billing in ISPs to security-related applications in firewalls and intrusion detection systems. Port-based, data packet inspection, and classical machine learning methods have been used extensively in the past, but their accuracy have been declined due to the dramatic changes in the Internet traffic, particularly the increase in encrypted traffic. With the proliferation of deep learning methods, researchers have recently investigated these methods for traffic classification task and reported high accuracy. In this article, we introduce a general framework for deep-learning-based traffic classification. We present commonly used deep learning methods and their application in traffic classification tasks. Then, we discuss open problems and their challenges, as well as opportunities for traffic classification.

Key facts. Cited by 27 papers in the map (data through 7 Oct 2026). First posted on arXiv 18 Oct 2018.

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Citing papers per year: 2021: 3; 2022: 8; 2023: 3; 2024: 6; 2025: 6; 2026: 1.

27 papers.

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  1. 2022Self-Supervised Contrastive Pre-Training For Time Series via Time-Frequency Consistencycited by 246
  2. 2022ET-BERT: A Contextualized Datagram Representation with Pre-training Transformers for Encrypted Traffic Classificationcited by 66
  3. 2022Fine-grained TLS services classification with reject optioncited by 14
  4. 2025LLMs Have Rhythm: Fingerprinting Large Language Models Using Inter-Token Times and Network Traffic Analysiscited by 7
  5. 2025CATS: Mitigating Correlation Shift for Multivariate Time Series Classificationcited by 7
  6. 2021Active Learning for Network Traffic Classification: A Technical Studycited by 5
  7. 2022When a RF Beats a CNN and GRU, Together -- A Comparison of Deep Learning and Classical Machine Learning Approaches for Encrypted Malware Traffic Classificationcited by 5
  8. 2023Graph Mining for Cybersecurity: A Surveycited by 5
  9. 2025Respond to Change with Constancy: Instruction-tuning with LLM for Non-I.I.D. Network Traffic Classificationcited by 4
  10. 2025FlowletFormer: Network Behavioral Semantic Aware Pre-training Model for Traffic Classificationcited by 3
  11. 2023Semi-Supervised End-To-End Contrastive Learning For Time Series Classificationcited by 2
  12. 2021Time-Distributed Feature Learning in Network Traffic Classification for Internet of Thingscited by 1
15 more
  1. 2022Deep Learning for Encrypted Traffic Classification and Unknown Data Detectioncited by 1
  2. 2022Can You Still See Me?: Reconstructing Robot Operations Over End-to-End Encrypted Channelscited by 1
  3. 2022Open-Source Framework for Encrypted Internet and Malicious Traffic Classificationcited by 1
  4. 2022Intrusion Detection in Internet of Things using Convolutional Neural Networkscited by 1
  5. 2024One Train for Two Tasks: An Encrypted Traffic Classification Framework Using Supervised Contrastive Learningcited by 1
  6. 2024Generic Multi-modal Representation Learning for Network Traffic Analysiscited by 1
  7. 2024Time-Distributed Feature Learning for Internet of Things Network Traffic Classificationcited by 1
  8. 2021Practical and Configurable Network Traffic Classification Using Probabilistic Machine Learningcited by 0
  9. 2023FedEdge AI-TC: A Semi-supervised Traffic Classification Method based on Trusted Federated Deep Learning for Mobile Edge Computingcited by 0
  10. 2024CBR -- Boosting Adaptive Classification By Retrieval of Encrypted Network Traffic with Out-of-distributioncited by 0
  11. 2024Representation Learning of Tangled Key-Value Sequence Data for Early Classificationcited by 0
  12. 2024Improving the network traffic classification using the Packet Vision approachcited by 0
  13. 2025Network Traffic Classification Using Machine Learning, Transformer, and Large Language Modelscited by 0
  14. 2025Dynamic Adaptive Parsing of Temporal and Cross-Variable Patterns for Network State Classificationcited by 0
  15. 2026Learning QoE from Packet-Level Measurements in Encrypted Video Conferencing Trafficcited by 0