# Deep Learning for Encrypted Traffic Classification: An Overview

arXiv: https://arxiv.org/abs/1810.07906 · HTML: https://mlsanity.com/paper/1810.07906/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Oct 2018.

## Built on by (most cited first, 27 of 27)

- [Self-Supervised Contrastive Pre-Training For Time Series via Time-Frequency Consistency](https://mlsanity.com/paper/2206.08496/) (2022)
- [ET-BERT: A Contextualized Datagram Representation with Pre-training Transformers for Encrypted Traffic Classification](https://mlsanity.com/paper/2202.06335/) (2022)
- [Fine-grained TLS services classification with reject option](https://mlsanity.com/paper/2202.11984/) (2022)
- [LLMs Have Rhythm: Fingerprinting Large Language Models Using Inter-Token Times and Network Traffic Analysis](https://mlsanity.com/paper/2502.20589/) (2025)
- [CATS: Mitigating Correlation Shift for Multivariate Time Series Classification](https://mlsanity.com/paper/2504.04283/) (2025)
- [Active Learning for Network Traffic Classification: A Technical Study](https://mlsanity.com/paper/2106.06933/) (2021)
- [When a RF Beats a CNN and GRU, Together -- A Comparison of Deep Learning and Classical Machine Learning Approaches for Encrypted Malware Traffic Classification](https://mlsanity.com/paper/2206.08004/) (2022)
- [Graph Mining for Cybersecurity: A Survey](https://mlsanity.com/paper/2304.00485/) (2023)
- [Respond to Change with Constancy: Instruction-tuning with LLM for Non-I.I.D. Network Traffic Classification](https://mlsanity.com/paper/2505.20866/) (2025)
- [FlowletFormer: Network Behavioral Semantic Aware Pre-training Model for Traffic Classification](https://mlsanity.com/paper/2508.19924/) (2025)
- [Semi-Supervised End-To-End Contrastive Learning For Time Series Classification](https://mlsanity.com/paper/2310.08848/) (2023)
- [Time-Distributed Feature Learning in Network Traffic Classification for Internet of Things](https://mlsanity.com/paper/2109.14696/) (2021)
- [Deep Learning for Encrypted Traffic Classification and Unknown Data Detection](https://mlsanity.com/paper/2203.15501/) (2022)
- [Can You Still See Me?: Reconstructing Robot Operations Over End-to-End Encrypted Channels](https://mlsanity.com/paper/2205.08426/) (2022)
- [Open-Source Framework for Encrypted Internet and Malicious Traffic Classification](https://mlsanity.com/paper/2206.10144/) (2022)
- [Intrusion Detection in Internet of Things using Convolutional Neural Networks](https://mlsanity.com/paper/2211.10062/) (2022)
- [One Train for Two Tasks: An Encrypted Traffic Classification Framework Using Supervised Contrastive Learning](https://mlsanity.com/paper/2402.07501/) (2024)
- [Generic Multi-modal Representation Learning for Network Traffic Analysis](https://mlsanity.com/paper/2405.02649/) (2024)
- [Time-Distributed Feature Learning for Internet of Things Network Traffic Classification](https://mlsanity.com/paper/2409.05096/) (2024)
- [Practical and Configurable Network Traffic Classification Using Probabilistic Machine Learning](https://mlsanity.com/paper/2107.06080/) (2021)
- [FedEdge AI-TC: A Semi-supervised Traffic Classification Method based on Trusted Federated Deep Learning for Mobile Edge Computing](https://mlsanity.com/paper/2308.06924/) (2023)
- [CBR -- Boosting Adaptive Classification By Retrieval of Encrypted Network Traffic with Out-of-distribution](https://mlsanity.com/paper/2403.11206/) (2024)
- [Representation Learning of Tangled Key-Value Sequence Data for Early Classification](https://mlsanity.com/paper/2404.07454/) (2024)
- [Improving the network traffic classification using the Packet Vision approach](https://mlsanity.com/paper/2412.19360/) (2024)
- [Network Traffic Classification Using Machine Learning, Transformer, and Large Language Models](https://mlsanity.com/paper/2503.02141/) (2025)
- [Dynamic Adaptive Parsing of Temporal and Cross-Variable Patterns for Network State Classification](https://mlsanity.com/paper/2509.11601/) (2025)
- [Learning QoE from Packet-Level Measurements in Encrypted Video Conferencing Traffic](https://mlsanity.com/paper/2601.06862/) (2026)

## 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.