FS-Net: A Flow Sequence Network For Encrypted Traffic Classification

By Chang Liu, Longtao He, Gang Xiong, Zigang Cao, Zhen Li. IEEE Conference on Computer Communications, 2019

442 citations recorded by the source corpus.

With more attention paid to user privacy and communication security, the volume of encrypted traffic rises sharply, which brings a huge challenge to traditional rule-based traffic classification methods. Combining machine learning algorithms and manual-design features has become the mainstream methods to solve this problem. However, these features depend on professional experience heavily, which needs lots of human effort. And these methods divide the encrypted traffic classification problem into piece-wise sub-problems, which could not guarantee the optimal solution. In this paper, we apply the recurrent neural network to the encrypted traffic classification problem and propose the Flow Sequence Network (FS-Net). The FS-Net is an end-to-end classification model that learns representative features from the raw flows, and then classifies them in a unified framework. Moreover, we adopt a multi-layer encoder-decoder structure which can mine the potential sequential characteristics of flows deeply, and import the reconstruction mechanism which can enhance the effectiveness of features. Our comprehensive experiments on the real-world dataset covering 18 applications indicate that FS-Net achieves an excellent performance (99.14% TPR, 0.05% FPR and 0.9906 FTF) and outperforms the state-of-the-art methods. Index Terms–Encrypted Traffic Classification, Recurrent Neural Network, Reconstruction Mechanism

Subfields: Artificial Intelligence, Computer Networks and Communications, Signal Processing

View Paper: https://www.semanticscholar.org/paper/3dbd549e92979d25935d59979e531b630b2d198b

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Citations in the corpus

ET-BERT: A Contextualized Datagram Representation with Pre-training Transformers for Encrypted Traffic Classification (2022, methodology): “Recently, deep learning methods [21, 22] automatically learn complicated patterns from the raw traffic (Figure 1(c)), and achieve remarkable performance improvement.”