mlsanityData to 7 Oct 2026

Deep Packet: A Novel Approach For Encrypted Traffic Classification Using Deep Learning

arXiv 1709.02656 ↗First posted 8 Sep 2017cs.LG cs.CR cs.NIcited by 69

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Abstract (arXiv)

Internet traffic classification has become more important with rapid growth of current Internet network and online applications. There have been numerous studies on this topic which have led to many different approaches. Most of these approaches use predefined features extracted by an expert in order to classify network traffic. In contrast, in this study, we propose a deep learning based approach which integrates both feature extraction and classification phases into one system. Our proposed scheme, called "Deep Packet," can handle both traffic characterization in which the network traffic is categorized into major classes (eg, FTP and P2P) and application identification in which end-user applications (eg, BitTorrent and Skype) identification is desired. Contrary to most of the current methods, Deep Packet can identify encrypted traffic and also distinguishes between VPN and non-VPN network traffic. After an initial pre-processing phase on data, packets are fed into Deep Packet framework that embeds stacked autoencoder and convolution neural network in order to classify network traffic. Deep packet with CNN as its classification model achieved recall of 0.98 in application identification task and 0.94 in traffic categorization task. To the best of our knowledge, Deep Packet outperforms all of the proposed classification methods on UNB ISCX VPN-nonVPN dataset.

Key facts. Cited by 69 papers in the map (data through 7 Oct 2026). First posted on arXiv 8 Sep 2017.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 10; 2022: 11; 2023: 9; 2024: 19; 2025: 8; 2026: 12.

69 papers.

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  1. 2022ET-BERT: A Contextualized Datagram Representation with Pre-training Transformers for Encrypted Traffic Classificationcited by 66
  2. 2024NetMamba: Efficient Network Traffic Classification via Pre-training Unidirectional Mambacited by 24
  3. 2021Deep Learning and Traffic Classification: Lessons learned from a commercial-grade dataset with hundreds of encrypted and zero-day applicationscited by 14
  4. 2023TFE-GNN: A Temporal Fusion Encoder Using Graph Neural Networks for Fine-grained Encrypted Traffic Classificationcited by 13
  5. 2022Extensible Machine Learning for Encrypted Network Traffic Application Labeling via Uncertainty Quantificationcited by 9
  6. 2024Communication-Efficient Large-Scale Distributed Deep Learning: A Comprehensive Surveycited by 9
  7. 2024Lens: A Knowledge-Guided Foundation Model for Network Trafficcited by 7
  8. 2021CGNN: Traffic Classification with Graph Neural Networkcited by 6
  9. 2022A Lightweight, Efficient and Explainable-by-Design Convolutional Neural Network for Internet Traffic Classificationcited by 6
  10. 2022Machine Learning for Encrypted Malicious Traffic Detection: Approaches, Datasets and Comparative Studycited by 6
  11. 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
  12. 2023Deep PackGen: A Deep Reinforcement Learning Framework for Adversarial Network Packet Generationcited by 5
38 more
  1. 2021Evaluating Resilience of Encrypted Traffic Classification Against Adversarial Evasion Attackscited by 4
  2. 2021Artificial Neural Network for Cybersecurity: A Comprehensive Reviewcited by 4
  3. 2021A First Look at Class Incremental Learning in Deep Learning Mobile Traffic Classificationcited by 4
  4. 2021Explainable Artificial Intelligence for Smart City Application: A Secure and Trusted Platformcited by 4
  5. 2024A Transformer-Based Framework for Payload Malware Detection and Classificationcited by 4
  6. 2024Towards Novel Malicious Packet Recognition: A Few-Shot Learning Approachcited by 4
  7. 2025Respond to Change with Constancy: Instruction-tuning with LLM for Non-I.I.D. Network Traffic Classificationcited by 4
  8. 2022ARCADE: Adversarially Regularized Convolutional Autoencoder for Network Anomaly Detectioncited by 3
  9. 2022Visualization Of Class Activation Maps To Explain AI Classification Of Network Packet Capturescited by 3
  10. 2024Semi-Supervised Learning for Anomaly Traffic Detection via Bidirectional Normalizing Flowscited by 3
  11. 2025Energy-Efficient Deep Learning for Traffic Classification on Microcontrollerscited by 3
  12. 2025FlowletFormer: Network Behavioral Semantic Aware Pre-training Model for Traffic Classificationcited by 3
  13. 2022Multi-view Multi-label Anomaly Network Traffic Classification based on MLP-Mixer Neural Networkcited by 2
  14. 2023Towards the Visualization of Aggregated Class Activation Maps to Analyse the Global Contribution of Class Featurescited by 2
  15. 2023Enhancing IoT Security via Automatic Network Traffic Analysis: The Transition from Machine Learning to Deep Learningcited by 2
  16. 2024Resource Allocation and Workload Scheduling for Large-Scale Distributed Deep Learning: A Surveycited by 2
  17. 2024Packet Inspection Transformer: A Self-Supervised Journey to Unseen Malware Detection with Few Samplescited by 2
  18. 2025Revolutionizing Encrypted Traffic Classification with MH-Net: A Multi-View Heterogeneous Graph Modelcited by 2
  19. 2025Hardware-Aware Neural Architecture Search for Encrypted Traffic Classification on Resource-Constrained Devicescited by 2
  20. 2021Encrypted Internet traffic classification using a supervised Spiking Neural Networkcited by 1
  21. 2021Federated Traffic Synthesizing and Classification Using Generative Adversarial Networkscited by 1
  22. 2021Detecting Attacks on IoT Devices using Featureless 1D-CNNcited by 1
  23. 2021Time-Distributed Feature Learning in Network Traffic Classification for Internet of Thingscited by 1
  24. 2022Deep Learning for Encrypted Traffic Classification and Unknown Data Detectioncited by 1
  25. 2022Can You Still See Me?: Reconstructing Robot Operations Over End-to-End Encrypted Channelscited by 1
  26. 2023AutoML4ETC: Automated Neural Architecture Search for Real-World Encrypted Traffic Classificationcited by 1
  27. 2023Listen to Minority: Encrypted Traffic Classification for Class Imbalance with Contrastive Pre-Trainingcited by 1
  28. 2024ServeFlow: A Fast-Slow Model Architecture for Network Traffic Analysiscited by 1
  29. 2024One Train for Two Tasks: An Encrypted Traffic Classification Framework Using Supervised Contrastive Learningcited by 1
  30. 2024Generic Multi-modal Representation Learning for Network Traffic Analysiscited by 1
  31. 2024Non-uniformity is All You Need: Efficient and Timely Encrypted Traffic Classification With ECHOcited by 1
  32. 2024Efficiently and Effectively: A Two-stage Approach to Balance Plaintext and Encrypted Text for Traffic Classificationcited by 1
  33. 2024MERLOT: A Distilled LLM-based Mixture-of-Experts Framework for Scalable Encrypted Traffic Classificationcited by 1
  34. 2026NetMamba+: A Framework of Pre-trained Models for Efficient and Accurate Network Traffic Classificationcited by 1
  35. 2026TrafficMoE: Heterogeneity-aware Mixture of Experts for Encrypted Traffic Classificationcited by 1
  36. 2026UniAlign: A Model-Agnostic Framework for Robust Network Traffic Classification under Distribution Shiftscited by 1
  37. 2026Interactive Analysis of Global Explanations using Aggregated Class Activation Maps for Network Datacited by 1
  38. 2026Pruned Traffic Trees: Native Semantic Compression with a Protocol-Structured Model Family for Encrypted Traffic Classificationcited by 1

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