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Deep Learning for Encrypted Traffic Classification: An Overview
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DOI：10.1109/MCOM.2019.1800819.png)
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摘要
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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.
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NEURAL-NETWORKS
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