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Encrypted TLS Traffic Classification on Cloud Platforms
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DOI：10.1109/TNET.2022.3191312.png)
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Abstract
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Nowadays, encryption technology has been widely used to protect user privacy. With the explosive growth of mobile Internet, encrypted TLS traffic rises sharply and occupies a great share of current Internet traffic. In reality, the classification of encrypted TLS traffic on cloud platforms brings a new challenge to traditional encrypted traffic classification methods, because some information such as certificates in the TLS flows is no longer effective. In this paper, we apply deep learning technology to the problem of encrypted TLS traffic classification on cloud platforms, and propose NeuTic, which takes the packet sequence of each TLS flow as the input, and effectively classifies raw TLS flows generated by many cloud applications. Our approach is able to automatically capture the long-range dependencies between elements in the packet sequences for robust and accurate encrypted TLS traffic classification. In NeuTic, we first convert each TLS flow into three attribute sequences. Then, we train a multi-application traffic classification model using our newly designed deep learning model. Finally, we use the well-trained classification model to classify new incoming TLS flows. We conduct comprehensive experiments on real-world application traces covering multiple cloud applications from three different companies. In addition, we compare our experimental results of NeuTic with two deep learning-based methods for encrypted traffic classification. NeuTic outperforms the state-of-the-art approaches in classification accuracy.
Keywords:
Cryptography
Markov processes
Cloud computing
Deep learning
Training
Recurrent neural networks
Learning systems
Encrypted traffic classification
cloud platform
self-attention mechanism
network security
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3.6
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4.4K
Citations:
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