Deep Learning for Encrypted Traffic Classification: An Overview

By Shahbaz Rezaei, Xin Liu. IEEE Communications Magazine, 2018

529 citations recorded by the source corpus.

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 has declined due to the dramatic changes in Internet traffic, particularly the increase in encrypted traffic. With the proliferation of deep learning methods, researchers have recently investigated these methods for traffic classification 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, challenges, and opportunities for traffic classification.

Subfields: Artificial Intelligence, Computer Networks and Communications, Information Systems

View Paper: https://www.semanticscholar.org/paper/6edf7ed0e68bcf98ebcb600343c1e08f27c1c62f

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Gang Xiong
2. Xiaojiang Du
3. Ke Xu
4. Meng Shen
5. Zhen Li
6. Liehuang Zhu
7. Theodoros Tsiligkaridis
8. Marinka Zitnik

In Computer Networks and Communications

1. Xiaojiang Du
2. Meng Shen
3. Liehuang Zhu
4. Marinka Zitnik

In Information Systems

1. Xiaojiang Du
2. Liehuang Zhu
3. Marinka Zitnik

Citations in the corpus

• Accurate Decentralized Application Identification via Encrypted Traffic Analysis Using Graph Neural Networks (2021, background): “As a concrete example, the summation function is injective over multisets [18].”
• ET-BERT: A Contextualized Datagram Representation with Pre-training Transformers for Encrypted Traffic Classification (2022, background): “Furthermore, due to the rapid development of encryption technology, traffic classification methods for a specific kind of encrypted traffic cannot adapt well to the new environment or unseen encryption strategies [29].”
• Self-Supervised Contrastive Pre-Training For Time Series via Time-Frequency Consistency (2022, background): “Time series plays important roles in many areas, including clinical diagnosis, traffic analysis, and climate science [1, 2, 3, 4, 5, 6].”